PrevisaoDesastre.ipynb

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      "source": [
        "#bibliotecas para pré-processamento\n",
        "import pandas as pd\n",
        "from datetime import datetime\n",
        "import numpy as np\n",
        "from google.colab import drive\n",
        "import requests\n",
        "\n",
        "#importar pacote do pandas para converter string para objeto\n",
        "from pandas.api.types import CategoricalDtype\n",
        "\n",
        "#bibliotecas para Matriz de Confusão\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "#biblioteca para Relatorio de Classificação\n",
        "from sklearn.metrics import classification_report , accuracy_score , roc_auc_score\n",
        "\n",
        "#biblioteca para iniciar o modelo de machine learning, Random Forest\n",
        "from io import StringIO\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.multioutput import MultiOutputClassifier\n",
        "\n",
        "#biblioteca para salvar o modelo treinado\n",
        "import joblib\n",
        "\n",
        "#biblioteca para manipulação do arquivo pkl\n",
        "import pickle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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          "base_uri": "https://localhost:8080/",
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          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content/drive/My Drive/Colab Notebooks/'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "#ok\n",
        "drive.mount('/content/drive')\n",
        "dadosTempo_dir = '/content/drive/My Drive/Colab Notebooks/'\n",
        "dadosTempo_dir"
      ]
    },
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        "outputId": "42abb305-cb50-4bea-8de3-aa9a2c18c623"
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      "outputs": [
        {
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          "data": {
            "text/plain": [
              "                  Região  Latitude  Longitude        Data   Chuva  Vento\n",
              "0      Alto de Pinheiros  -235.611   -467.011  01/10/2024    5.00   0.56\n",
              "1             Anhanguera  -234.978   -467.523  01/10/2024   26.00   0.58\n",
              "2             Aricanduva  -235.583   -465.011  01/10/2024   24.50   0.19\n",
              "3            Artur Alvim  -235.394   -464.917  01/10/2024   23.20   0.00\n",
              "4            Barra Funda  -235.275   -466.583  01/10/2024   22.10   0.65\n",
              "...                  ...       ...        ...         ...     ...    ...\n",
              "10094         São Rafael  -235.667   -464.500  09/01/2025   76.05  14.68\n",
              "10095          Sapopemba  -235.667   -465.000  09/01/2025   94.86  60.39\n",
              "10096              Saúde  -236.000   -466.333  09/01/2025  288.02  97.54\n",
              "10097                 Sé  -235.505   -466.333  09/01/2025  122.18  52.72\n",
              "10098       Vila Mariana  -236.000   -467.000  09/01/2025  228.31  33.97\n",
              "\n",
              "[10099 rows x 6 columns]"
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              "      <th></th>\n",
              "      <th>Região</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>Longitude</th>\n",
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              "      <th>0</th>\n",
              "      <td>Alto de Pinheiros</td>\n",
              "      <td>-235.611</td>\n",
              "      <td>-467.011</td>\n",
              "      <td>01/10/2024</td>\n",
              "      <td>5.00</td>\n",
              "      <td>0.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Anhanguera</td>\n",
              "      <td>-234.978</td>\n",
              "      <td>-467.523</td>\n",
              "      <td>01/10/2024</td>\n",
              "      <td>26.00</td>\n",
              "      <td>0.58</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Aricanduva</td>\n",
              "      <td>-235.583</td>\n",
              "      <td>-465.011</td>\n",
              "      <td>01/10/2024</td>\n",
              "      <td>24.50</td>\n",
              "      <td>0.19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Artur Alvim</td>\n",
              "      <td>-235.394</td>\n",
              "      <td>-464.917</td>\n",
              "      <td>01/10/2024</td>\n",
              "      <td>23.20</td>\n",
              "      <td>0.00</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Barra Funda</td>\n",
              "      <td>-235.275</td>\n",
              "      <td>-466.583</td>\n",
              "      <td>01/10/2024</td>\n",
              "      <td>22.10</td>\n",
              "      <td>0.65</td>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>10094</th>\n",
              "      <td>São Rafael</td>\n",
              "      <td>-235.667</td>\n",
              "      <td>-464.500</td>\n",
              "      <td>09/01/2025</td>\n",
              "      <td>76.05</td>\n",
              "      <td>14.68</td>\n",
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              "    <tr>\n",
              "      <th>10095</th>\n",
              "      <td>Sapopemba</td>\n",
              "      <td>-235.667</td>\n",
              "      <td>-465.000</td>\n",
              "      <td>09/01/2025</td>\n",
              "      <td>94.86</td>\n",
              "      <td>60.39</td>\n",
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              "    <tr>\n",
              "      <th>10096</th>\n",
              "      <td>Saúde</td>\n",
              "      <td>-236.000</td>\n",
              "      <td>-466.333</td>\n",
              "      <td>09/01/2025</td>\n",
              "      <td>288.02</td>\n",
              "      <td>97.54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10097</th>\n",
              "      <td>Sé</td>\n",
              "      <td>-235.505</td>\n",
              "      <td>-466.333</td>\n",
              "      <td>09/01/2025</td>\n",
              "      <td>122.18</td>\n",
              "      <td>52.72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10098</th>\n",
              "      <td>Vila Mariana</td>\n",
              "      <td>-236.000</td>\n",
              "      <td>-467.000</td>\n",
              "      <td>09/01/2025</td>\n",
              "      <td>228.31</td>\n",
              "      <td>33.97</td>\n",
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              "summary": "{\n  \"name\": \"df_CGE\",\n  \"rows\": 10099,\n  \"fields\": [\n    {\n      \"column\": \"Regi\\u00e3o\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 85,\n        \"samples\": [\n          \"S\\u00e3o Miguel Paulista\",\n          \"Alto de Pinheiros\",\n          \"Rio Pequeno\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.851075974199711,\n        \"min\": -238.49,\n        \"max\": -234.035,\n        \"num_unique_values\": 54,\n        \"samples\": [\n          -235.5,\n          -234.44,\n          -235.45\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0771340657239037,\n        \"min\": -469.17,\n        \"max\": -463.986,\n        \"num_unique_values\": 54,\n        \"samples\": [\n          -465.333,\n          -467.15,\n          -469.17\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Data\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 101,\n        \"samples\": [\n          \"24/12/2024\",\n          \"25/11/2024\",\n          \"06/12/2024\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Chuva\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 87.88819197180746,\n        \"min\": 0.0,\n        \"max\": 299.94,\n        \"num_unique_values\": 8521,\n        \"samples\": [\n          246.71,\n          79.14,\n          219.21\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Vento\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29.326968043278217,\n        \"min\": 0.0,\n        \"max\": 99.97,\n        \"num_unique_values\": 6342,\n        \"samples\": [\n          32.48,\n          46.59,\n          37.34\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "#ok\n",
        "df_CGE = pd.read_csv('/content/drive/My Drive/Colab Notebooks/CGE2.csv', encoding='latin1' , sep=';')\n",
        "df_CGE = df_CGE[['Região','Latitude','Longitude', 'Data','Chuva','Vento']]\n",
        "df_CGE=df_CGE.dropna()\n",
        "df_CGE"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#ok\n",
        "df_historico = pd.read_csv('/content/drive/My Drive/Colab Notebooks/historico.csv', encoding='latin1' , sep=';')\n",
        "df_historico = df_historico[['Região', 'Data','Chuva','Vento','Queda Arvore', 'Alagamento']]\n",
        "df_historico=df_historico.dropna()\n",
        "df_historico['Data'] = pd.to_datetime(df_historico['Data'], format='%d/%m/%Y')\n",
        "df_historico.dtypes"
      ],
      "metadata": {
        "id": "BH5i-1NWPXpu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "e1623f61-8d4b-4d6b-9dfb-247c59067cba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Região                  object\n",
              "Data            datetime64[ns]\n",
              "Chuva                  float64\n",
              "Vento                  float64\n",
              "Queda Arvore             int64\n",
              "Alagamento               int64\n",
              "dtype: object"
            ],
            "text/html": [
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              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Região</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Data</th>\n",
              "      <td>datetime64[ns]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chuva</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Vento</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Queda Arvore</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alagamento</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
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    {
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      "execution_count": null,
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          "height": 423
        },
        "outputId": "b2c63dc0-d248-4e5c-8450-87c37411db6f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Latitude  Longitude  Tipo  prob                    Label\n",
              "0   -23.5505   -46.6333     1    45            árvore normal\n",
              "1   -23.5610   -46.6570     1    30            árvore normal\n",
              "2   -23.5730   -46.6410     1    50  árvore propensa a queda\n",
              "3   -23.5800   -46.6350     1    20            árvore normal\n",
              "4   -23.5900   -46.6200     1    60  árvore propensa a queda\n",
              "..       ...        ...   ...   ...                      ...\n",
              "94  -23.8700   -46.3400     1    40            árvore normal\n",
              "95  -23.8800   -46.3300     1    30            árvore normal\n",
              "96  -23.8900   -46.3200     1    50  árvore propensa a queda\n",
              "97  -23.9000   -46.3100     1    20            árvore normal\n",
              "98  -23.9100   -46.3000     1    60  árvore propensa a queda\n",
              "\n",
              "[99 rows x 5 columns]"
            ],
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Latitude</th>\n",
              "      <th>Longitude</th>\n",
              "      <th>Tipo</th>\n",
              "      <th>prob</th>\n",
              "      <th>Label</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-23.5505</td>\n",
              "      <td>-46.6333</td>\n",
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              "      <td>45</td>\n",
              "      <td>árvore normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-23.5610</td>\n",
              "      <td>-46.6570</td>\n",
              "      <td>1</td>\n",
              "      <td>30</td>\n",
              "      <td>árvore normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-23.5730</td>\n",
              "      <td>-46.6410</td>\n",
              "      <td>1</td>\n",
              "      <td>50</td>\n",
              "      <td>árvore propensa a queda</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-23.5800</td>\n",
              "      <td>-46.6350</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>árvore normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-23.5900</td>\n",
              "      <td>-46.6200</td>\n",
              "      <td>1</td>\n",
              "      <td>60</td>\n",
              "      <td>árvore propensa a queda</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>-23.8700</td>\n",
              "      <td>-46.3400</td>\n",
              "      <td>1</td>\n",
              "      <td>40</td>\n",
              "      <td>árvore normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>-23.8800</td>\n",
              "      <td>-46.3300</td>\n",
              "      <td>1</td>\n",
              "      <td>30</td>\n",
              "      <td>árvore normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>-23.8900</td>\n",
              "      <td>-46.3200</td>\n",
              "      <td>1</td>\n",
              "      <td>50</td>\n",
              "      <td>árvore propensa a queda</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>-23.9000</td>\n",
              "      <td>-46.3100</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>árvore normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>-23.9100</td>\n",
              "      <td>-46.3000</td>\n",
              "      <td>1</td>\n",
              "      <td>60</td>\n",
              "      <td>árvore propensa a queda</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>99 rows × 5 columns</p>\n",
              "</div>\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "  <div id=\"id_b9d3e429-18af-43de-8c8d-682091481451\">\n",
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              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_arvore')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_b9d3e429-18af-43de-8c8d-682091481451 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_arvore');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_arvore",
              "summary": "{\n  \"name\": \"df_arvore\",\n  \"rows\": 99,\n  \"fields\": [\n    {\n      \"column\": \"Latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1363085938379209,\n        \"min\": -23.97,\n        \"max\": -23.4035,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          -23.5011,\n          -23.95,\n          -23.4035\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1313292192079486,\n        \"min\": -46.7523,\n        \"max\": -46.24,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          -46.3986,\n          -46.26,\n          -46.701\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tipo\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prob\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13,\n        \"min\": 20,\n        \"max\": 60,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Label\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"\\u00e1rvore propensa a queda\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "#ok\n",
        "resultado_cnn_path = '/content/drive/My Drive/Colab Notebooks/ResultadoCNN.csv'\n",
        "df_arvore = pd.read_csv('/content/drive/My Drive/Colab Notebooks/ResultadoCNN.csv', encoding='UTF-8' , sep=';')\n",
        "#converter Data em Datetime\n",
        "#df_arvore['Data'] = pd.to_datetime(df_arvore['Data'], format='%d/%m/%Y')\n",
        "#remover a coluna Data\n",
        "df_arvore = df_arvore.drop(columns=['Data'])\n",
        "df_arvore"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8BAOoUfMtLe_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "outputId": "aa5e216e-ae26-4894-bfd7-a0187aba47c6"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "\"['Data'] not found in axis\"",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-32-02b13d7262bf>\u001b[0m in \u001b[0;36m<cell line: 5>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mdf_arvore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_arvore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# Remove espaços ao redor dos nomes de colunas\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mdf_arvore\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_arvore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Data'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0mdf_arvore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   5342\u001b[0m                 \u001b[0mweight\u001b[0m  \u001b[0;36m1.0\u001b[0m     \u001b[0;36m0.8\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5343\u001b[0m         \"\"\"\n\u001b[0;32m-> 5344\u001b[0;31m         return super().drop(\n\u001b[0m\u001b[1;32m   5345\u001b[0m             \u001b[0mlabels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5346\u001b[0m             \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   4709\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4710\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4711\u001b[0;31m                 \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4712\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4713\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[0;34m(self, labels, axis, level, errors, only_slice)\u001b[0m\n\u001b[1;32m   4751\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4752\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4753\u001b[0;31m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4754\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_axis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4755\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, errors)\u001b[0m\n\u001b[1;32m   6998\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6999\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 7000\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{labels[mask].tolist()} not found in axis\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   7001\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   7002\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: \"['Data'] not found in axis\""
          ]
        }
      ],
      "source": [
        "#ok---devo excluir esse escopo\n",
        "#df_arvore['Data'] = pd.to_datetime(df_arvore['Data'], format='mixed')\n",
        "df_arvore.columns = df_arvore.columns.str.strip()  # Remove espaços ao redor dos nomes de colunas\n",
        "\n",
        "df_arvore = df_arvore.drop(columns=['Data'])\n",
        "df_arvore.head"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "urXsSHFZbeBt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "754c1b0d-777d-473f-ee54-c951f193d490"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Região               object\n",
              "Latitude            float64\n",
              "Longitude           float64\n",
              "Data         datetime64[ns]\n",
              "Chuva               float64\n",
              "Vento               float64\n",
              "dtype: object"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Região</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Latitude</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Longitude</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Data</th>\n",
              "      <td>datetime64[ns]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chuva</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Vento</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "#ok\n",
        "df_CGE['Chuva'] = df_CGE['Chuva'].astype(float)\n",
        "df_CGE['Data'] = pd.to_datetime(df_CGE['Data'], format='%d/%m/%Y')\n",
        "df_CGE.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lH8lKN02BWYD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209
        },
        "outputId": "a480d299-fd8a-4425-d214-a203f38e3ae1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Tipo           int64\n",
              "Latitude     float64\n",
              "Longitude    float64\n",
              "Label         object\n",
              "dtype: object"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Tipo</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Latitude</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Longitude</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Label</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "#ok\n",
        "df_bueiro = pd.read_csv('/content/drive/My Drive/Colab Notebooks/Bueiro.csv',encoding='utf-8' , sep=';')\n",
        "df_bueiro = df_bueiro[['Tipo','Latitude','Longitude', 'Data', 'Label']]\n",
        "df_bueiro = df_bueiro.dropna()\n",
        "#remover coluna Data do df_bueiro\n",
        "df_bueiro = df_bueiro.drop(columns=['Data'])\n",
        "#df_bueiro['Data'] = pd.to_datetime(df_bueiro['Data'], format='%d/%m/%Y')\n",
        "df_bueiro.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WAgSTovGtxZx",
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              "              Região  Latitude  Longitude       Data  Chuva  Vento\n",
              "0  Alto de Pinheiros  -235.611   -467.011 2024-10-01    5.0   0.56\n",
              "1         Anhanguera  -234.978   -467.523 2024-10-01   26.0   0.58\n",
              "2         Aricanduva  -235.583   -465.011 2024-10-01   24.5   0.19\n",
              "3        Artur Alvim  -235.394   -464.917 2024-10-01   23.2   0.00\n",
              "4        Barra Funda  -235.275   -466.583 2024-10-01   22.1   0.65"
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              "      <th>Região</th>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <th>2</th>\n",
              "      <td>Aricanduva</td>\n",
              "      <td>-235.583</td>\n",
              "      <td>-465.011</td>\n",
              "      <td>2024-10-01</td>\n",
              "      <td>24.5</td>\n",
              "      <td>0.19</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Artur Alvim</td>\n",
              "      <td>-235.394</td>\n",
              "      <td>-464.917</td>\n",
              "      <td>2024-10-01</td>\n",
              "      <td>23.2</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Barra Funda</td>\n",
              "      <td>-235.275</td>\n",
              "      <td>-466.583</td>\n",
              "      <td>2024-10-01</td>\n",
              "      <td>22.1</td>\n",
              "      <td>0.65</td>\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_CGE",
              "summary": "{\n  \"name\": \"df_CGE\",\n  \"rows\": 10099,\n  \"fields\": [\n    {\n      \"column\": \"Regi\\u00e3o\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 85,\n        \"samples\": [\n          \"S\\u00e3o Miguel Paulista\",\n          \"Alto de Pinheiros\",\n          \"Rio Pequeno\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.851075974199711,\n        \"min\": -238.49,\n        \"max\": -234.035,\n        \"num_unique_values\": 54,\n        \"samples\": [\n          -235.5,\n          -234.44,\n          -235.45\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0771340657239037,\n        \"min\": -469.17,\n        \"max\": -463.986,\n        \"num_unique_values\": 54,\n        \"samples\": [\n          -465.333,\n          -467.15,\n          -469.17\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Data\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2024-10-01 00:00:00\",\n        \"max\": \"2025-01-09 00:00:00\",\n        \"num_unique_values\": 101,\n        \"samples\": [\n          \"2024-12-24 00:00:00\",\n          \"2024-11-25 00:00:00\",\n          \"2024-12-06 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Chuva\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 87.88819197180746,\n        \"min\": 0.0,\n        \"max\": 299.94,\n        \"num_unique_values\": 8521,\n        \"samples\": [\n          246.71,\n          79.14,\n          219.21\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Vento\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29.326968043278217,\n        \"min\": 0.0,\n        \"max\": 99.97,\n        \"num_unique_values\": 6342,\n        \"samples\": [\n          32.48,\n          46.59,\n          37.34\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "df_CGE.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Pre-processamento (criar coluna 'indice' para identificador unico de cada região)\n",
        "df_CGE['indice'] = pd.factorize(df_CGE['Região'])[0]\n",
        "df_CGE.head()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Cee-4cBClGOI",
        "outputId": "4b80eb6b-b0d6-4f19-bad4-2a3064f8239e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Região  Latitude  Longitude       Data  Chuva  Vento  indice\n",
              "0  Alto de Pinheiros  -235.611   -467.011 2024-10-01    5.0   0.56       0\n",
              "1         Anhanguera  -234.978   -467.523 2024-10-01   26.0   0.58       1\n",
              "2         Aricanduva  -235.583   -465.011 2024-10-01   24.5   0.19       2\n",
              "3        Artur Alvim  -235.394   -464.917 2024-10-01   23.2   0.00       3\n",
              "4        Barra Funda  -235.275   -466.583 2024-10-01   22.1   0.65       4"
            ],
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              "      <td>22.1</td>\n",
              "      <td>0.65</td>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-e39ea874-99f4-4afe-a6f1-96fb18fe5732 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e39ea874-99f4-4afe-a6f1-96fb18fe5732');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-83537032-0ea0-4fc7-a5f3-fc96b99282bf\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-83537032-0ea0-4fc7-a5f3-fc96b99282bf')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-83537032-0ea0-4fc7-a5f3-fc96b99282bf button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_CGE",
              "summary": "{\n  \"name\": \"df_CGE\",\n  \"rows\": 10099,\n  \"fields\": [\n    {\n      \"column\": \"Regi\\u00e3o\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 85,\n        \"samples\": [\n          \"S\\u00e3o Miguel Paulista\",\n          \"Alto de Pinheiros\",\n          \"Rio Pequeno\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.851075974199711,\n        \"min\": -238.49,\n        \"max\": -234.035,\n        \"num_unique_values\": 54,\n        \"samples\": [\n          -235.5,\n          -234.44,\n          -235.45\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0771340657239037,\n        \"min\": -469.17,\n        \"max\": -463.986,\n        \"num_unique_values\": 54,\n        \"samples\": [\n          -465.333,\n          -467.15,\n          -469.17\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Data\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2024-10-01 00:00:00\",\n        \"max\": \"2025-01-09 00:00:00\",\n        \"num_unique_values\": 101,\n        \"samples\": [\n          \"2024-12-24 00:00:00\",\n          \"2024-11-25 00:00:00\",\n          \"2024-12-06 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Chuva\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 87.88819197180746,\n        \"min\": 0.0,\n        \"max\": 299.94,\n        \"num_unique_values\": 8521,\n        \"samples\": [\n          246.71,\n          79.14,\n          219.21\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Vento\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29.326968043278217,\n        \"min\": 0.0,\n        \"max\": 99.97,\n        \"num_unique_values\": 6342,\n        \"samples\": [\n          32.48,\n          46.59,\n          37.34\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"indice\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24,\n        \"min\": 0,\n        \"max\": 84,\n        \"num_unique_values\": 85,\n        \"samples\": [\n          78,\n          0,\n          68\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Remover as colunas indesejadas que começam com 'Região_' ou 'indice_'\n",
        "colunas_indesejadas = [col for col in df_CGE.columns if col.startswith('Região_') or col.startswith('indice_')]\n",
        "df_CGE = df_CGE.drop(columns=colunas_indesejadas)\n",
        "print(df_CGE.columns)\n",
        "df_CGE.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "kLFo4qkwpXOc",
        "outputId": "c414bf97-d70b-4790-a190-98bad6bd845b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['Região', 'Latitude', 'Longitude', 'Data', 'Chuva', 'Vento', 'indice'], dtype='object')\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Região  Latitude  Longitude       Data  Chuva  Vento  indice\n",
              "0  Alto de Pinheiros  -235.611   -467.011 2024-10-01    5.0   0.56       0\n",
              "1         Anhanguera  -234.978   -467.523 2024-10-01   26.0   0.58       1\n",
              "2         Aricanduva  -235.583   -465.011 2024-10-01   24.5   0.19       2\n",
              "3        Artur Alvim  -235.394   -464.917 2024-10-01   23.2   0.00       3\n",
              "4        Barra Funda  -235.275   -466.583 2024-10-01   22.1   0.65       4"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-db71884c-4d2b-4a0e-94d6-e94e4c690867\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Região</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>Longitude</th>\n",
              "      <th>Data</th>\n",
              "      <th>Chuva</th>\n",
              "      <th>Vento</th>\n",
              "      <th>indice</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Alto de Pinheiros</td>\n",
              "      <td>-235.611</td>\n",
              "      <td>-467.011</td>\n",
              "      <td>2024-10-01</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Anhanguera</td>\n",
              "      <td>-234.978</td>\n",
              "      <td>-467.523</td>\n",
              "      <td>2024-10-01</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0.58</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Aricanduva</td>\n",
              "      <td>-235.583</td>\n",
              "      <td>-465.011</td>\n",
              "      <td>2024-10-01</td>\n",
              "      <td>24.5</td>\n",
              "      <td>0.19</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Artur Alvim</td>\n",
              "      <td>-235.394</td>\n",
              "      <td>-464.917</td>\n",
              "      <td>2024-10-01</td>\n",
              "      <td>23.2</td>\n",
              "      <td>0.00</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Barra Funda</td>\n",
              "      <td>-235.275</td>\n",
              "      <td>-466.583</td>\n",
              "      <td>2024-10-01</td>\n",
              "      <td>22.1</td>\n",
              "      <td>0.65</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-db71884c-4d2b-4a0e-94d6-e94e4c690867')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-db71884c-4d2b-4a0e-94d6-e94e4c690867 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-db71884c-4d2b-4a0e-94d6-e94e4c690867');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-03fc350d-4bab-47e8-a795-03191a90d420\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-03fc350d-4bab-47e8-a795-03191a90d420')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-03fc350d-4bab-47e8-a795-03191a90d420 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_CGE",
              "summary": "{\n  \"name\": \"df_CGE\",\n  \"rows\": 10099,\n  \"fields\": [\n    {\n      \"column\": \"Regi\\u00e3o\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 85,\n        \"samples\": [\n          \"S\\u00e3o Miguel Paulista\",\n          \"Alto de Pinheiros\",\n          \"Rio Pequeno\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.851075974199711,\n        \"min\": -238.49,\n        \"max\": -234.035,\n        \"num_unique_values\": 54,\n        \"samples\": [\n          -235.5,\n          -234.44,\n          -235.45\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0771340657239037,\n        \"min\": -469.17,\n        \"max\": -463.986,\n        \"num_unique_values\": 54,\n        \"samples\": [\n          -465.333,\n          -467.15,\n          -469.17\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Data\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2024-10-01 00:00:00\",\n        \"max\": \"2025-01-09 00:00:00\",\n        \"num_unique_values\": 101,\n        \"samples\": [\n          \"2024-12-24 00:00:00\",\n          \"2024-11-25 00:00:00\",\n          \"2024-12-06 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Chuva\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 87.88819197180746,\n        \"min\": 0.0,\n        \"max\": 299.94,\n        \"num_unique_values\": 8521,\n        \"samples\": [\n          246.71,\n          79.14,\n          219.21\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Vento\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29.326968043278217,\n        \"min\": 0.0,\n        \"max\": 99.97,\n        \"num_unique_values\": 6342,\n        \"samples\": [\n          32.48,\n          46.59,\n          37.34\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"indice\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24,\n        \"min\": 0,\n        \"max\": 84,\n        \"num_unique_values\": 85,\n        \"samples\": [\n          78,\n          0,\n          68\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Verificar se as colunas estão presentes\n",
        "colunas_disponiveis = ['Data', 'Latitude', 'Longitude', 'Região', 'tipo', 'prob%']\n",
        "colunas_existentes = [col for col in colunas_disponiveis if col in df_CGE.columns]\n",
        "\n",
        "# Caso não todas as colunas estejam disponíveis, filtrar apenas as existentes\n",
        "if len(colunas_existentes) < len(colunas_disponiveis):\n",
        "    print(f\"As seguintes colunas estão faltando: {set(colunas_disponiveis) - set(colunas_existentes)}\")\n",
        "\n",
        "# Preserve as colunas originais para o CSV final\n",
        "dados_originais = df_CGE[colunas_existentes]\n",
        "\n",
        "\n",
        "#unir CGE com arvore e CGE com bueiro\n",
        "# Fazer o merge dos dois dataframes com base em 'Latitude' e 'Longitude'\n",
        "df_comb_CGE_arvore = pd.merge(df_CGE, df_arvore, on=['Latitude', 'Longitude'], how='left')\n",
        "\n",
        "#df_comb_CGE_bueiro = pd.merge(df_CGE, df_bueiro.drop(columns=['Latitude','Longitude']), on=['Data'])\n",
        "# Fazer o merge dos dois dataframes com base em 'Latitude' e 'Longitude'\n",
        "df_comb_CGE_bueiro = pd.merge(df_CGE, df_bueiro, on=['Latitude', 'Longitude'], how='left')\n",
        "df_comb_CGE_bueiro"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "7Ec6Mbg7qk_g",
        "outputId": "ea8d9a33-b89f-4deb-b923-ba08e8b3b49b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "As seguintes colunas estão faltando: {'prob%', 'tipo'}\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Região  Latitude  Longitude       Data   Chuva  Vento  \\\n",
              "0      Alto de Pinheiros  -235.611   -467.011 2024-10-01    5.00   0.56   \n",
              "1             Anhanguera  -234.978   -467.523 2024-10-01   26.00   0.58   \n",
              "2             Aricanduva  -235.583   -465.011 2024-10-01   24.50   0.19   \n",
              "3            Artur Alvim  -235.394   -464.917 2024-10-01   23.20   0.00   \n",
              "4            Barra Funda  -235.275   -466.583 2024-10-01   22.10   0.65   \n",
              "...                  ...       ...        ...        ...     ...    ...   \n",
              "10094         São Rafael  -235.667   -464.500 2025-01-09   76.05  14.68   \n",
              "10095          Sapopemba  -235.667   -465.000 2025-01-09   94.86  60.39   \n",
              "10096              Saúde  -236.000   -466.333 2025-01-09  288.02  97.54   \n",
              "10097                 Sé  -235.505   -466.333 2025-01-09  122.18  52.72   \n",
              "10098       Vila Mariana  -236.000   -467.000 2025-01-09  228.31  33.97   \n",
              "\n",
              "       indice  Tipo Label  \n",
              "0           0   NaN   NaN  \n",
              "1           1   NaN   NaN  \n",
              "2           2   NaN   NaN  \n",
              "3           3   NaN   NaN  \n",
              "4           4   NaN   NaN  \n",
              "...       ...   ...   ...  \n",
              "10094      79   NaN   NaN  \n",
              "10095      80   NaN   NaN  \n",
              "10096      81   NaN   NaN  \n",
              "10097      82   NaN   NaN  \n",
              "10098      83   NaN   NaN  \n",
              "\n",
              "[10099 rows x 9 columns]"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Região</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>Longitude</th>\n",
              "      <th>Data</th>\n",
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              "      <th>0</th>\n",
              "      <td>Alto de Pinheiros</td>\n",
              "      <td>-235.611</td>\n",
              "      <td>-467.011</td>\n",
              "      <td>2024-10-01</td>\n",
              "      <td>5.00</td>\n",
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              "      <td>0</td>\n",
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              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Anhanguera</td>\n",
              "      <td>-234.978</td>\n",
              "      <td>-467.523</td>\n",
              "      <td>2024-10-01</td>\n",
              "      <td>26.00</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Aricanduva</td>\n",
              "      <td>-235.583</td>\n",
              "      <td>-465.011</td>\n",
              "      <td>2024-10-01</td>\n",
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              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Artur Alvim</td>\n",
              "      <td>-235.394</td>\n",
              "      <td>-464.917</td>\n",
              "      <td>2024-10-01</td>\n",
              "      <td>23.20</td>\n",
              "      <td>0.00</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Barra Funda</td>\n",
              "      <td>-235.275</td>\n",
              "      <td>-466.583</td>\n",
              "      <td>2024-10-01</td>\n",
              "      <td>22.10</td>\n",
              "      <td>0.65</td>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>10094</th>\n",
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              "      <td>-235.667</td>\n",
              "      <td>-464.500</td>\n",
              "      <td>2025-01-09</td>\n",
              "      <td>76.05</td>\n",
              "      <td>14.68</td>\n",
              "      <td>79</td>\n",
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              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>10095</th>\n",
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              "      <td>-235.667</td>\n",
              "      <td>-465.000</td>\n",
              "      <td>2025-01-09</td>\n",
              "      <td>94.86</td>\n",
              "      <td>60.39</td>\n",
              "      <td>80</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10096</th>\n",
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              "      <td>-236.000</td>\n",
              "      <td>-466.333</td>\n",
              "      <td>2025-01-09</td>\n",
              "      <td>288.02</td>\n",
              "      <td>97.54</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>-466.333</td>\n",
              "      <td>2025-01-09</td>\n",
              "      <td>122.18</td>\n",
              "      <td>52.72</td>\n",
              "      <td>82</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>10098</th>\n",
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              "      <td>-467.000</td>\n",
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              "      <td>228.31</td>\n",
              "      <td>33.97</td>\n",
              "      <td>83</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "  </script>\n",
              "</div>\n",
              "\n",
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              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
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              "\n",
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              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_comb_CGE_bueiro')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_comb_CGE_bueiro');\n",
              "      }\n",
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              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_comb_CGE_bueiro",
              "repr_error": "Out of range float values are not JSON compliant: nan"
            }
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Reconstruindo a coluna 'Região' a partir das colunas dummies para CGE com arvore\n",
        "regiao_cols_arv = [col for col in df_comb_CGE_arvore.columns if col.startswith('Região_')]\n",
        "\n",
        "# Reconstruindo a coluna 'Região' a partir das colunas dummies para CGE com bueiro\n",
        "regiao_cols_bue = [col for col in df_comb_CGE_bueiro.columns if col.startswith('Região_')]"
      ],
      "metadata": {
        "id": "cb6vH7liqlGc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Excluir as colunas de Região antes de separar os dados (usando a nova coluna 'indice' como feature)\n",
        "df_comb_CGE_arvore['indice'] = pd.factorize(df_comb_CGE_arvore['Região'])[0]\n",
        "\n",
        "df_comb_CGE_bueiro['indice'] = pd.factorize(df_comb_CGE_bueiro['Região'])[0]\n",
        "\n",
        "df_historico['indice'] = pd.factorize(df_historico['Região'])[0]\n",
        "#df_encoded = df_encoded.drop(columns=regiao_cols + ['Data', 'Latitude', 'Longitude', 'Label'])\n"
      ],
      "metadata": {
        "id": "D7rNds3Pq5Su"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Criar as labels para Queda de Árvore e Alagamento\n",
        "df_comb_CGE_arvore['Label_Queda'] = (df_comb_CGE_arvore['Tipo'] == 1).astype(int)  # 1 para Queda de Árvore\n",
        "df_comb_CGE_bueiro['Label_Alagamento'] = (df_comb_CGE_bueiro['Tipo'] == 2).astype(int)  # 1 para Alagamento\n"
      ],
      "metadata": {
        "id": "uFH93YCTrOp7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Verificar o resultado\n",
        "print(df_comb_CGE_arvore[['Tipo', 'Label_Queda']].head())\n",
        "print(df_comb_CGE_bueiro[['Tipo', 'Label_Alagamento']].head())"
      ],
      "metadata": {
        "id": "4ukD2gkwrOx5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c0cf514d-fc72-4f27-ba34-df4833f64d48"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Tipo  Label_Queda\n",
            "0   NaN            0\n",
            "1   NaN            0\n",
            "2   NaN            0\n",
            "3   NaN            0\n",
            "4   NaN            0\n",
            "   Tipo  Label_Alagamento\n",
            "0   NaN                 0\n",
            "1   NaN                 0\n",
            "2   NaN                 0\n",
            "3   NaN                 0\n",
            "4   NaN                 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "DYwgIZTh0HKu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "MAy7F9Hu0HTP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "##a partir daki do chatgpt"
      ],
      "metadata": {
        "id": "bKY5NzYAq5gZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. Modelo para prever Alagamento\n",
        "X_alagamento = df_historico[['Vento', 'Chuva', 'indice']]  # Variáveis preditoras\n",
        "y_alagamento = df_historico['Alagamento']  # Variável de resposta\n",
        "X_alagamento.head()"
      ],
      "metadata": {
        "id": "Yx4KpraOlGWG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "086c02a1-b755-4b83-d7ba-fa7c3329571b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Vento  Chuva  indice\n",
              "0   27.7   11.0       0\n",
              "1   26.6   14.3       1\n",
              "2   10.6   12.1       2\n",
              "3   21.4   10.9       3\n",
              "4   22.2    8.5       4"
            ],
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X_alagamento",
              "summary": "{\n  \"name\": \"X_alagamento\",\n  \"rows\": 2400,\n  \"fields\": [\n    {\n      \"column\": \"Vento\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.981717269465035,\n        \"min\": 10.0,\n        \"max\": 100.5,\n        \"num_unique_values\": 246,\n        \"samples\": [\n          15.9,\n          10.5,\n          12.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Chuva\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.858228215195652,\n        \"min\": 0.0,\n        \"max\": 20.0,\n        \"num_unique_values\": 201,\n        \"samples\": [\n          13.2,\n          1.7,\n          15.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"indice\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23,\n        \"min\": 0,\n        \"max\": 79,\n        \"num_unique_values\": 80,\n        \"samples\": [\n          30,\n          0,\n          22\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Dividir em dados de treino e teste\n",
        "X_train_alag, X_test_alag, y_train_alag, y_test_alag = train_test_split(X_alagamento, y_alagamento, test_size=0.2, random_state=42)\n"
      ],
      "metadata": {
        "id": "uh3CT8CflGeB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Treinar o modelo de alagamento\n",
        "modelo_alag = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "modelo_alag.fit(X_train_alag, y_train_alag)"
      ],
      "metadata": {
        "id": "ovchvEKklPc0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "outputId": "2d4147f2-736a-45f0-f7a1-33e64e79713f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-2 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-2 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-2 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-2 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-2 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-2 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-2 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(random_state=42)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fazer predições\n",
        "alagamento_preds = modelo_alag.predict(df_CGE[['Vento', 'Chuva', 'indice']])\n",
        "\n"
      ],
      "metadata": {
        "id": "yWMXLMkXlPkW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. Relatório de Classificação - Alagamento\n",
        "\n",
        "# Fazer predições no conjunto de teste\n",
        "y_pred_alag = modelo_alag.predict(X_test_alag)\n",
        "\n",
        "# Geração do relatório de classificação\n",
        "relatorio_alag = classification_report(y_test_alag, y_pred_alag, target_names=['Sem Alagamento', 'Alagamento'])\n",
        "\n",
        "# Exibe o relatório de classificação para o modelo de alagamento\n",
        "print(\"Relatório de Classificação - Alagamento:\")\n",
        "print(relatorio_alag)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PwbdGinG3A4n",
        "outputId": "cfad7745-ebb8-4caf-81d0-49d1e7501878"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Relatório de Classificação - Alagamento:\n",
            "                precision    recall  f1-score   support\n",
            "\n",
            "Sem Alagamento       0.69      0.85      0.76       330\n",
            "    Alagamento       0.33      0.17      0.22       150\n",
            "\n",
            "      accuracy                           0.64       480\n",
            "     macro avg       0.51      0.51      0.49       480\n",
            "  weighted avg       0.58      0.64      0.59       480\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2. Modelo para prever Queda de Árvore\n",
        "X_queda = df_historico[['Vento', 'Chuva', 'indice']]\n",
        "y_queda = df_historico['Queda Arvore']"
      ],
      "metadata": {
        "id": "PCpIhLkKlP32"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Dividir em dados de treino e teste\n",
        "X_train_queda, X_test_queda, y_train_queda, y_test_queda = train_test_split(X_queda, y_queda, test_size=0.2, random_state=42)\n"
      ],
      "metadata": {
        "id": "w8lKKUIflYqF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Treinar o modelo de queda de árvore\n",
        "modelo_queda = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "modelo_queda.fit(X_train_queda, y_train_queda)"
      ],
      "metadata": {
        "id": "2FDb_bJWlYyH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "outputId": "4bc8c51d-6bd7-4a5e-9d8b-ccec9c43b9ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-3 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-3 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-3 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-3 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-3 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-3 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-3 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-3 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-3 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(random_state=42)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fazer predições\n",
        "queda_preds = modelo_queda.predict(df_CGE[['Vento', 'Chuva', 'indice']])"
      ],
      "metadata": {
        "id": "x3Zh6QdjlY49"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 2. Relatório de Classificação - Queda de Árvore\n",
        "\n",
        "# Fazer predições no conjunto de teste\n",
        "y_pred_queda = modelo_queda.predict(X_test_queda)\n",
        "\n",
        "# Geração do relatório de classificação\n",
        "relatorio_queda = classification_report(y_test_queda, y_pred_queda, target_names=['Sem Queda de Árvore', 'Queda de Árvore'])\n",
        "\n",
        "# Exibe o relatório de classificação para o modelo de queda de árvore\n",
        "print(\"\\nRelatório de Classificação - Queda de Árvore:\")\n",
        "print(relatorio_queda)"
      ],
      "metadata": {
        "id": "UIC3gIY04zJe",
        "outputId": "441f49f0-a328-43ab-fdab-856486f14875",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Relatório de Classificação - Queda de Árvore:\n",
            "                     precision    recall  f1-score   support\n",
            "\n",
            "Sem Queda de Árvore       0.59      0.69      0.64       269\n",
            "    Queda de Árvore       0.50      0.40      0.44       211\n",
            "\n",
            "           accuracy                           0.56       480\n",
            "          macro avg       0.55      0.54      0.54       480\n",
            "       weighted avg       0.55      0.56      0.55       480\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "conf_mat = confusion_matrix(y_test_queda, y_pred_queda)\n",
        "plt.figure(figsize=(10, 8))\n",
        "sns.heatmap(conf_mat, annot=True, fmt='d', cmap='Blues')\n",
        "plt.xlabel('Classe Prevista')\n",
        "plt.ylabel('Classe Verdadeira')\n",
        "plt.title('Matriz de Confusão Queda de Árvore')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 719
        },
        "id": "vVMj_XR2tPeR",
        "outputId": "31168c78-a389-4091-aefb-d311a5b97789"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "conf_mat = confusion_matrix(y_test_alag, y_pred_alag)\n",
        "plt.figure(figsize=(10, 8))\n",
        "sns.heatmap(conf_mat, annot=True, fmt='d', cmap='Blues')\n",
        "plt.xlabel('Classe Prevista')\n",
        "plt.ylabel('Classe Verdadeira')\n",
        "plt.title('Matriz de Confusão Alagamento')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "DYvwv_OLtptK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Combinar as predições e dados\n",
        "df_CGE['Pred_Alagamento'] = alagamento_preds\n",
        "df_CGE['Pred_Queda_Arvore'] = queda_preds\n",
        "df_CGE"
      ],
      "metadata": {
        "id": "TXvP7ntOlQAS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#na tabela df_CGE criar coluna prob%\n",
        "df_CGE['prob_alag%'] = 0.0\n",
        "#se Pred_Alagamento = 1 entao prob%= (Chuva/100)*0.5\n",
        "df_CGE.loc[df_CGE['Pred_Alagamento'] == 0, 'prob_alag%'] = df_CGE['Chuva'] / 100 * 0.5\n",
        "\n",
        "#se Pred_Queda_Arvore = 1 entao , if 15.28 <= Vento <=18.6 , entao prob% = 0.75\n",
        "df_CGE['prob_queda%'] = 0.0\n",
        "# Se a coluna 'Pred_Queda_Arvore' for 1 e o valor de 'Vento' estiver entre 15.28 e 18.6, definir 'prob%' como 0.75\n",
        "df_CGE.loc[(df_CGE['Pred_Queda_Arvore'] == 0) & (df_CGE['Vento'].between(15.28, 18.6)), 'prob_queda%'] = 0.75\n",
        "df_CGE.loc[(df_CGE['Pred_Queda_Arvore'] == 0) & (df_CGE['Vento'].between(21.67, 300)), 'prob_queda%'] = 1.00\n",
        "\n",
        "df_CGE\n"
      ],
      "metadata": {
        "id": "8qfiR9x_oZu7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Salvar o resultado final\n",
        "resultado_alagamento = df_CGE[['Região', 'Latitude', 'Longitude', 'Data', 'Vento', 'Chuva', 'indice', 'Pred_Alagamento', 'prob_alag%']]\n",
        "resultado_queda = df_CGE[['Região', 'Latitude', 'Longitude', 'Data', 'Vento', 'Chuva', 'indice', 'Pred_Queda_Arvore', 'prob_queda%']]\n",
        "resultado_alagamento"
      ],
      "metadata": {
        "id": "sZ2RB-gmlGmx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#fazer merge com resultado_queda e resultado_alagamento\n",
        "resultado_final = pd.merge(resultado_queda, resultado_alagamento, on=['Região', 'Latitude', 'Longitude', 'Data', 'Vento', 'Chuva', 'indice','Pred_Alagamento', 'prob_alag%','Pred_Queda_Arvore', 'prob_queda%'], how='outer')\n",
        "# Salvar em arquivo CSV\n",
        "resultado_final.to_csv('/content/drive/My Drive/Colab Notebooks/resultado_final.csv', index=False)"
      ],
      "metadata": {
        "id": "YITNG9jVlGtk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "sB11IhIflw7H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lzdMUxKWlxHV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "YuIwE6hMlxPu"
      },
      "execution_count": null,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Ab0fJiFnlxYt"
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      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
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      "metadata": {
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      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "654ydAQJlxpi"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}