diff --git a/Rainfall prediction b/Rainfall prediction new file mode 100644 index 000000000..b498fd495 --- /dev/null +++ b/Rainfall prediction @@ -0,0 +1 @@ +/ diff --git a/Rainfall_Prediction/ANN2.ipynb b/Rainfall_Prediction/ANN2.ipynb new file mode 100644 index 000000000..6f89133e2 --- /dev/null +++ b/Rainfall_Prediction/ANN2.ipynb @@ -0,0 +1,1694 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "folTicD17J9n" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import tensorflow as tf\n", + "import matplotlib.pyplot as plt\n", + "from datetime import datetime\n", + "import urllib.request\n", + "\n", + "from geopy.geocoders import Nominatim\n", + "pd.set_option('display.max_columns', None)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "UGGcNJ23hole" + }, + "outputs": [], + "source": [ + "start_date = input(\"Enter start date in yyyy/mm/dd format :\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "9wuWPeWfhqCX" + }, + "outputs": [], + "source": [ + "start_date = start_date.replace('/','')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "HWwbKFg5htkR" + }, + "outputs": [], + "source": [ + "end_date = input(\"Enter end date in yyyy/mm/dd format :\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "eJmmjcmZht8I" + }, + "outputs": [], + "source": [ + "end_date = end_date.replace('/','')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "7Yq7rSiHhuZp" + }, + "outputs": [], + "source": [ + "address = input(\"Enter city name:\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "JZPeXqBVh7YT" + }, + "outputs": [], + "source": [ + "geolocator = Nominatim(user_agent=\"Kartikey\")\n", + "location= geolocator.geocode(address)\n", + "latitude1 = location.latitude\n", + "longitude1 = location.longitude" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "1I0Z9AF_iBpT" + }, + "outputs": [], + "source": [ + "#url = 'https://power.larc.nasa.gov//api/temporal/daily/point?parameters=T2M_MAX,T2M_MIN,RH2M,PRECTOTCORR,PS,WS10M_RANGE&community=RE&longitude=longitude1&latitude=latitude1&start=start_date&end=end_date&format=CSV'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "uiRAS_89iBgb" + }, + "outputs": [], + "source": [ + "#urllib.request.urlretrieve(url,'weather.csv')\n", + "#if someone wanst to take Data from NASA API" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "id": "__qXxzj57cPT", + "outputId": "2145d208-3f68-4530-c941-6b5d0367092c" + }, + "outputs": [ + { + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "summary": "{\n 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\n" + ], + "text/plain": [ + " T2M_MAX T2M_MIN TS RH2M PRECTOTCORR WS10M\n", + "DATE \n", + "1982/1/1 14.94 2.58 2.76 35.19 -1.000000e-08 2.09\n", + "1982/1/2 15.50 2.85 3.21 34.69 -1.000000e-08 2.22\n", + "1982/1/3 14.44 2.90 2.33 29.31 -1.000000e-08 1.66\n", + "1982/1/4 7.72 -0.85 2.22 73.31 2.550000e+00 2.23\n", + "1982/1/5 10.55 0.20 3.15 69.56 8.000000e-02 1.83" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.drop(columns=['YEAR','MO','DY','date'],axis=1,inplace=True)\n", + "df.set_index(['DATE'],inplace = True)\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "q9regsE4-Mdr" + }, + "outputs": [], + "source": [ + "from sklearn.preprocessing import MinMaxScaler\n", + "sc = MinMaxScaler()\n", + "df_scaled = sc.fit_transform(df)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "1BKygoAU-ahC" + }, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "train, test= train_test_split(df_scaled, test_size=0.2, shuffle = False)\n", + "\n", + "x_train, y_train, x_test, y_test= [],[],[],[]\n", + "for i in range (1,len(train)):\n", + " x_train.append(train[i-1])\n", + " y_train.append(train[i])\n", + "for i in range (1,len(test)):\n", + " x_test.append(test[i-1])\n", + " y_test.append(test[i])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "6NC64WJrANo3" + }, + "outputs": [], + "source": [ + "x_train, y_train= np.array(x_train), np.array(y_train)\n", + "x_test, y_test= np.array(x_test), np.array(y_test)\n", + "x_train.shape, y_train.shape, x_test.shape, y_test.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "kSJ3lwGj-cNB" + }, + "outputs": [], + "source": [ + "from keras.models import Sequential\n", + "from keras.layers import Dense, Activation\n", + "from keras.optimizers import Adam\n", + "from keras.models import load_model\n", + "from keras.layers import Dropout" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "D4ts0ci8_Aom", + "outputId": "2454eff7-330c-48e0-affc-8cff3f3997df" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_3\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " dense_12 (Dense) (None, 6) 42 \n", + " \n", + " dense_13 (Dense) (None, 10) 70 \n", + " \n", + " dense_14 (Dense) (None, 10) 110 \n", + " \n", + " dense_15 (Dense) (None, 6) 66 \n", + " \n", + "=================================================================\n", + "Total params: 288 (1.12 KB)\n", + "Trainable params: 288 (1.12 KB)\n", + "Non-trainable params: 0 (0.00 Byte)\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "model = Sequential()\n", + "model.add(Dense(6,input_dim=6, activation= 'relu'))\n", + "model.add(Dense(10,activation= 'relu'))\n", + "model.add(Dense(10,activation= 'relu'))\n", + "model.add(Dense(6))\n", + "model.summary()\n", + "model.compile(optimizer = 'adam', loss='mse', metrics = ['accuracy'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "6UUulEhj_MbH", + "outputId": "bbf49793-b526-4ecf-8247-83a45d39020f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/100\n", + "765/765 [==============================] - 3s 3ms/step - loss: 0.0386 - accuracy: 0.2630 - val_loss: 0.0086 - val_accuracy: 0.4389\n", + "Epoch 2/100\n", + "765/765 [==============================] - 3s 4ms/step - loss: 0.0061 - accuracy: 0.5014 - val_loss: 0.0035 - val_accuracy: 0.6720\n", + "Epoch 3/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 0.0027 - accuracy: 0.6723 - val_loss: 0.0026 - val_accuracy: 0.7107\n", + "Epoch 4/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 0.0023 - accuracy: 0.6940 - val_loss: 0.0024 - val_accuracy: 0.7118\n", + "Epoch 5/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 0.0023 - accuracy: 0.6840 - val_loss: 0.0022 - val_accuracy: 0.7076\n", + "Epoch 6/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 0.0021 - accuracy: 0.6806 - val_loss: 0.0020 - val_accuracy: 0.7090\n", + "Epoch 7/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 0.0020 - accuracy: 0.6876 - val_loss: 0.0017 - val_accuracy: 0.7128\n", + "Epoch 8/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 0.0017 - accuracy: 0.6985 - val_loss: 0.0014 - val_accuracy: 0.7191\n", + "Epoch 9/100\n", + "765/765 [==============================] - 2s 3ms/step - loss: 0.0014 - accuracy: 0.7109 - val_loss: 0.0013 - val_accuracy: 0.7516\n", + "Epoch 10/100\n", + "765/765 [==============================] - 2s 3ms/step - loss: 0.0011 - accuracy: 0.7339 - val_loss: 7.5722e-04 - val_accuracy: 0.8077\n", + "Epoch 11/100\n", + "765/765 [==============================] - 2s 3ms/step - loss: 7.0269e-04 - accuracy: 0.7659 - val_loss: 8.4469e-04 - val_accuracy: 0.8367\n", + "Epoch 12/100\n", + "765/765 [==============================] - 4s 5ms/step - loss: 9.8439e-04 - accuracy: 0.7746 - val_loss: 6.0713e-04 - val_accuracy: 0.8371\n", + "Epoch 13/100\n", + "765/765 [==============================] - 2s 3ms/step - loss: 5.8648e-04 - accuracy: 0.7911 - val_loss: 5.4790e-04 - val_accuracy: 0.8095\n", + "Epoch 14/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 9.2020e-04 - accuracy: 0.7820 - val_loss: 6.0054e-04 - val_accuracy: 0.8531\n", + "Epoch 15/100\n", + "765/765 [==============================] - 2s 3ms/step - loss: 5.7194e-04 - accuracy: 0.8027 - val_loss: 0.0012 - val_accuracy: 0.8479\n", + "Epoch 16/100\n", + "765/765 [==============================] - 2s 3ms/step - loss: 7.7421e-04 - accuracy: 0.7894 - val_loss: 4.4504e-04 - val_accuracy: 0.8510\n", + "Epoch 17/100\n", + "765/765 [==============================] - 1s 2ms/step - loss: 6.7700e-04 - accuracy: 0.8021 - val_loss: 0.0014 - val_accuracy: 0.8580\n", + "Epoch 18/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 5.9089e-04 - accuracy: 0.7961 - val_loss: 4.8999e-04 - val_accuracy: 0.8318\n", + "Epoch 19/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 6.3389e-04 - accuracy: 0.7910 - val_loss: 0.0015 - val_accuracy: 0.7369\n", + "Epoch 20/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 5.7776e-04 - accuracy: 0.8141 - val_loss: 7.5806e-04 - val_accuracy: 0.8493\n", + "Epoch 21/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 6.0076e-04 - accuracy: 0.8190 - val_loss: 4.1925e-04 - val_accuracy: 0.8789\n", + "Epoch 22/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 4.9738e-04 - accuracy: 0.8111 - val_loss: 7.0301e-04 - val_accuracy: 0.8294\n", + "Epoch 23/100\n", + "765/765 [==============================] - 3s 3ms/step - loss: 7.0181e-04 - accuracy: 0.8115 - val_loss: 9.9837e-04 - val_accuracy: 0.8412\n", + "Epoch 24/100\n", + "765/765 [==============================] - 2s 3ms/step - loss: 2.9561e-04 - accuracy: 0.8466 - val_loss: 2.8250e-04 - val_accuracy: 0.8999\n", + "Epoch 25/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 5.1497e-04 - accuracy: 0.8116 - val_loss: 3.3430e-04 - val_accuracy: 0.8782\n", + "Epoch 26/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 5.7609e-04 - accuracy: 0.8320 - val_loss: 1.7954e-04 - val_accuracy: 0.9114\n", + "Epoch 27/100\n", + "765/765 [==============================] - 1s 2ms/step - loss: 3.7956e-04 - accuracy: 0.8149 - val_loss: 0.0010 - val_accuracy: 0.7209\n", + "Epoch 28/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 5.2407e-04 - accuracy: 0.8245 - val_loss: 2.1917e-04 - val_accuracy: 0.8890\n", + "Epoch 29/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 4.0516e-04 - accuracy: 0.8197 - val_loss: 4.7180e-04 - val_accuracy: 0.8255\n", + "Epoch 30/100\n", + "765/765 [==============================] - 2s 3ms/step - loss: 4.3054e-04 - accuracy: 0.8239 - val_loss: 6.9579e-04 - val_accuracy: 0.8193\n", + "Epoch 31/100\n", + "765/765 [==============================] - 3s 3ms/step - loss: 4.9100e-04 - accuracy: 0.8327 - val_loss: 2.6214e-04 - val_accuracy: 0.8782\n", + "Epoch 32/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 3.5544e-04 - accuracy: 0.8353 - val_loss: 5.0181e-04 - val_accuracy: 0.8182\n", + "Epoch 33/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 5.2400e-04 - accuracy: 0.8078 - val_loss: 2.9688e-04 - val_accuracy: 0.8880\n", + "Epoch 34/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 2.5333e-04 - accuracy: 0.8520 - val_loss: 5.0843e-04 - val_accuracy: 0.8974\n", + "Epoch 35/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 5.9600e-04 - accuracy: 0.8033 - val_loss: 6.7029e-04 - val_accuracy: 0.8852\n", + "Epoch 36/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 3.3972e-04 - accuracy: 0.8494 - val_loss: 3.7866e-04 - val_accuracy: 0.8883\n", + "Epoch 37/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 5.0221e-04 - accuracy: 0.8228 - val_loss: 7.3441e-04 - val_accuracy: 0.8890\n", + "Epoch 38/100\n", + "765/765 [==============================] - 2s 3ms/step - loss: 2.7475e-04 - accuracy: 0.8552 - val_loss: 3.3849e-04 - val_accuracy: 0.8702\n", + "Epoch 39/100\n", + "765/765 [==============================] - 3s 3ms/step - loss: 3.4895e-04 - accuracy: 0.8359 - val_loss: 7.8056e-04 - val_accuracy: 0.8524\n", + "Epoch 40/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 5.2756e-04 - accuracy: 0.8031 - val_loss: 2.2845e-04 - val_accuracy: 0.9170\n", + "Epoch 41/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 3.3543e-04 - accuracy: 0.8523 - val_loss: 2.6927e-04 - val_accuracy: 0.8768\n", + "Epoch 42/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 4.6479e-04 - accuracy: 0.8275 - val_loss: 6.7672e-04 - val_accuracy: 0.8444\n", + "Epoch 43/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 3.1750e-04 - accuracy: 0.8603 - val_loss: 3.6808e-04 - val_accuracy: 0.9184\n", + "Epoch 44/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 4.4273e-04 - accuracy: 0.8288 - val_loss: 4.7178e-04 - val_accuracy: 0.8894\n", + "Epoch 45/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 3.5482e-04 - accuracy: 0.8543 - val_loss: 4.8723e-04 - val_accuracy: 0.8154\n", + "Epoch 46/100\n", + "765/765 [==============================] - 3s 3ms/step - loss: 3.8599e-04 - accuracy: 0.8252 - val_loss: 1.9441e-04 - val_accuracy: 0.8856\n", + "Epoch 47/100\n", + "765/765 [==============================] - 2s 3ms/step - loss: 4.0953e-04 - accuracy: 0.8286 - val_loss: 8.0585e-04 - val_accuracy: 0.8266\n", + "Epoch 48/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 3.2558e-04 - accuracy: 0.8393 - val_loss: 5.4792e-04 - val_accuracy: 0.8744\n", + "Epoch 49/100\n", + "765/765 [==============================] - 1s 2ms/step - loss: 4.3664e-04 - accuracy: 0.8228 - val_loss: 2.6603e-04 - val_accuracy: 0.9075\n", + "Epoch 50/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 3.9724e-04 - accuracy: 0.8483 - val_loss: 5.3828e-04 - val_accuracy: 0.8611\n", + "Epoch 51/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 2.8446e-04 - accuracy: 0.8354 - val_loss: 3.9957e-04 - val_accuracy: 0.8674\n", + "Epoch 52/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 4.2552e-04 - accuracy: 0.8108 - val_loss: 4.0915e-04 - val_accuracy: 0.8573\n", + "Epoch 53/100\n", + "765/765 [==============================] - 3s 3ms/step - loss: 4.6410e-04 - accuracy: 0.8422 - val_loss: 0.0010 - val_accuracy: 0.8800\n", + "Epoch 54/100\n", + "765/765 [==============================] - 3s 4ms/step - loss: 2.6332e-04 - accuracy: 0.8574 - val_loss: 3.5245e-04 - val_accuracy: 0.8786\n", + "Epoch 55/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 3.6666e-04 - accuracy: 0.8108 - val_loss: 2.9094e-04 - val_accuracy: 0.8601\n", + "Epoch 56/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 4.3633e-04 - accuracy: 0.8348 - val_loss: 6.5436e-04 - val_accuracy: 0.8367\n", + "Epoch 57/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 3.5413e-04 - accuracy: 0.8314 - val_loss: 3.3127e-04 - val_accuracy: 0.8852\n", + "Epoch 58/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 3.3492e-04 - accuracy: 0.8564 - val_loss: 4.7048e-04 - val_accuracy: 0.8137\n", + "Epoch 59/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 3.3813e-04 - accuracy: 0.8152 - val_loss: 3.5791e-04 - val_accuracy: 0.8814\n", + "Epoch 60/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 4.2085e-04 - accuracy: 0.8529 - val_loss: 6.7902e-04 - val_accuracy: 0.9051\n", + "Epoch 61/100\n", + "765/765 [==============================] - 2s 3ms/step - loss: 3.1736e-04 - accuracy: 0.8467 - val_loss: 4.8169e-04 - val_accuracy: 0.8636\n", + "Epoch 62/100\n", + "765/765 [==============================] - 3s 4ms/step - loss: 2.8151e-04 - accuracy: 0.8252 - val_loss: 3.4861e-04 - val_accuracy: 0.8158\n", + "Epoch 63/100\n", + "765/765 [==============================] - 2s 3ms/step - loss: 4.1174e-04 - accuracy: 0.8308 - val_loss: 2.0667e-04 - val_accuracy: 0.8918\n", + "Epoch 64/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 3.9016e-04 - accuracy: 0.8190 - val_loss: 2.3722e-04 - val_accuracy: 0.8758\n", + "Epoch 65/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 3.3200e-04 - accuracy: 0.8539 - val_loss: 3.2746e-04 - val_accuracy: 0.8207\n", + "Epoch 66/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 2.6037e-04 - accuracy: 0.8496 - val_loss: 6.5928e-04 - val_accuracy: 0.8800\n", + "Epoch 67/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 5.3014e-04 - accuracy: 0.8138 - val_loss: 2.8869e-04 - val_accuracy: 0.8803\n", + "Epoch 68/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 1.5446e-04 - accuracy: 0.8689 - val_loss: 2.1925e-04 - val_accuracy: 0.8880\n", + "Epoch 69/100\n", + "765/765 [==============================] - 2s 3ms/step - loss: 4.3371e-04 - accuracy: 0.8376 - val_loss: 4.1280e-04 - val_accuracy: 0.8901\n", + "Epoch 70/100\n", + "765/765 [==============================] - 3s 3ms/step - loss: 4.1083e-04 - accuracy: 0.8276 - val_loss: 2.6705e-04 - val_accuracy: 0.8412\n", + "Epoch 71/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 2.4452e-04 - accuracy: 0.8609 - val_loss: 5.9043e-04 - val_accuracy: 0.9020\n", + "Epoch 72/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 3.7249e-04 - accuracy: 0.8448 - val_loss: 4.4901e-04 - val_accuracy: 0.9002\n", + "Epoch 73/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 4.2085e-04 - accuracy: 0.8361 - val_loss: 6.7509e-04 - val_accuracy: 0.8650\n", + "Epoch 74/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 3.3264e-04 - accuracy: 0.8253 - val_loss: 4.6824e-04 - val_accuracy: 0.8524\n", + "Epoch 75/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 3.2240e-04 - accuracy: 0.8361 - val_loss: 4.0208e-04 - val_accuracy: 0.8447\n", + "Epoch 76/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 3.3533e-04 - accuracy: 0.8454 - val_loss: 4.0860e-04 - val_accuracy: 0.9086\n", + "Epoch 77/100\n", + "765/765 [==============================] - 2s 3ms/step - loss: 3.7096e-04 - accuracy: 0.8399 - val_loss: 2.3823e-04 - val_accuracy: 0.8786\n", + "Epoch 78/100\n", + "765/765 [==============================] - 2s 3ms/step - loss: 4.0161e-04 - accuracy: 0.8180 - val_loss: 2.6671e-04 - val_accuracy: 0.8765\n", + "Epoch 79/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 2.5925e-04 - accuracy: 0.8439 - val_loss: 3.4745e-04 - val_accuracy: 0.8639\n", + "Epoch 80/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 3.8136e-04 - accuracy: 0.8338 - val_loss: 6.6353e-04 - val_accuracy: 0.8423\n", + "Epoch 81/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 3.2282e-04 - accuracy: 0.8358 - val_loss: 3.0574e-04 - val_accuracy: 0.8943\n", + "Epoch 82/100\n", + "765/765 [==============================] - 1s 2ms/step - loss: 3.4873e-04 - accuracy: 0.8319 - val_loss: 8.3516e-04 - val_accuracy: 0.8018\n", + "Epoch 83/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 3.6741e-04 - accuracy: 0.8478 - val_loss: 2.1217e-04 - val_accuracy: 0.8925\n", + "Epoch 84/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 2.6058e-04 - accuracy: 0.8606 - val_loss: 3.5599e-04 - val_accuracy: 0.8876\n", + "Epoch 85/100\n", + "765/765 [==============================] - 3s 3ms/step - loss: 4.5559e-04 - accuracy: 0.8022 - val_loss: 6.3708e-04 - val_accuracy: 0.7781\n", + "Epoch 86/100\n", + "765/765 [==============================] - 2s 3ms/step - loss: 2.3939e-04 - accuracy: 0.8662 - val_loss: 3.1624e-04 - val_accuracy: 0.9027\n", + "Epoch 87/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 4.0492e-04 - accuracy: 0.8307 - val_loss: 5.3134e-04 - val_accuracy: 0.8001\n", + "Epoch 88/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 2.8698e-04 - accuracy: 0.8502 - val_loss: 2.8069e-04 - val_accuracy: 0.8967\n", + "Epoch 89/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 3.2638e-04 - accuracy: 0.8518 - val_loss: 5.7906e-04 - val_accuracy: 0.7149\n", + "Epoch 90/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 4.0032e-04 - accuracy: 0.8046 - val_loss: 3.6899e-04 - val_accuracy: 0.8667\n", + "Epoch 91/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 2.7519e-04 - accuracy: 0.8665 - val_loss: 4.2344e-04 - val_accuracy: 0.9037\n", + "Epoch 92/100\n", + "765/765 [==============================] - 2s 3ms/step - loss: 3.9572e-04 - accuracy: 0.8221 - val_loss: 5.8726e-04 - val_accuracy: 0.7404\n", + "Epoch 93/100\n", + "765/765 [==============================] - 3s 3ms/step - loss: 2.1019e-04 - accuracy: 0.8457 - val_loss: 3.4222e-04 - val_accuracy: 0.7973\n", + "Epoch 94/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 4.4317e-04 - accuracy: 0.8275 - val_loss: 3.2608e-04 - val_accuracy: 0.8876\n", + "Epoch 95/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 2.9529e-04 - accuracy: 0.8513 - val_loss: 6.1303e-04 - val_accuracy: 0.8510\n", + "Epoch 96/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 3.2148e-04 - accuracy: 0.8504 - val_loss: 4.1394e-04 - val_accuracy: 0.8109\n", + "Epoch 97/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 3.6965e-04 - accuracy: 0.8167 - val_loss: 3.3235e-04 - val_accuracy: 0.8241\n", + "Epoch 98/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 2.5371e-04 - accuracy: 0.8429 - val_loss: 6.7129e-04 - val_accuracy: 0.7226\n", + "Epoch 99/100\n", + "765/765 [==============================] - 2s 2ms/step - loss: 3.6595e-04 - accuracy: 0.8337 - val_loss: 6.0367e-04 - val_accuracy: 0.9016\n", + "Epoch 100/100\n", + "765/765 [==============================] - 2s 3ms/step - loss: 3.0859e-04 - accuracy: 0.8686 - val_loss: 6.9744e-04 - val_accuracy: 0.8904\n" + ] + } + ], + "source": [ + "history = model.fit(x_train,y_train, validation_data = (x_test, y_test), epochs = 100, batch_size=15, shuffle=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 733 + }, + "id": "ThZV9hoSqGE0", + "outputId": "b7a0dc91-1426-4b4e-d3ce-c6463ad26ced" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + " 
matplotlib.pyplot.show
def show(*args, **kwargs)
/usr/local/lib/python3.10/dist-packages/matplotlib/pyplot.pyDisplay all open figures.\n",
+              "\n",
+              "Parameters\n",
+              "----------\n",
+              "block : bool, optional\n",
+              "    Whether to wait for all figures to be closed before returning.\n",
+              "\n",
+              "    If `True` block and run the GUI main loop until all figure windows\n",
+              "    are closed.\n",
+              "\n",
+              "    If `False` ensure that all figure windows are displayed and return\n",
+              "    immediately.  In this case, you are responsible for ensuring\n",
+              "    that the event loop is running to have responsive figures.\n",
+              "\n",
+              "    Defaults to True in non-interactive mode and to False in interactive\n",
+              "    mode (see `.pyplot.isinteractive`).\n",
+              "\n",
+              "See Also\n",
+              "--------\n",
+              "ion : Enable interactive mode, which shows / updates the figure after\n",
+              "      every plotting command, so that calling ``show()`` is not necessary.\n",
+              "ioff : Disable interactive mode.\n",
+              "savefig : Save the figure to an image file instead of showing it on screen.\n",
+              "\n",
+              "Notes\n",
+              "-----\n",
+              "**Saving figures to file and showing a window at the same time**\n",
+              "\n",
+              "If you want an image file as well as a user interface window, use\n",
+              "`.pyplot.savefig` before `.pyplot.show`. At the end of (a blocking)\n",
+              "``show()`` the figure is closed and thus unregistered from pyplot. Calling\n",
+              "`.pyplot.savefig` afterwards would save a new and thus empty figure. This\n",
+              "limitation of command order does not apply if the show is non-blocking or\n",
+              "if you keep a reference to the figure and use `.Figure.savefig`.\n",
+              "\n",
+              "**Auto-show in jupyter notebooks**\n",
+              "\n",
+              "The jupyter backends (activated via ``%matplotlib inline``,\n",
+              "``%matplotlib notebook``, or ``%matplotlib widget``), call ``show()`` at\n",
+              "the end of every cell by default. Thus, you usually don't have to call it\n",
+              "explicitly there.
\n", + " \n", + "
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.rcParams[\"figure.figsize\"] = (10,6)\n", + "plt.plot(history.history['accuracy'])\n", + "plt.plot(history.history['val_accuracy'])\n", + "plt.title('Training and validation accuracy')\n", + "plt.xlabel('Epochs')\n", + "plt.ylabel('Accuracy')\n", + "plt.legend(['train', 'val'], loc='upper left')\n", + "plt.show" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "4IVlZx2mwtm3", + "outputId": "afe12265-8114-4b2b-d6af-92a25704e888" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "90/90 [==============================] - 0s 2ms/step\n" + ] + } + ], + "source": [ + "final_pred=model.predict(y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "27aLtOb4wiWj", + "outputId": "dd76ccd1-5f15-4615-ee70-45c9935f56cf" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean Absolute Error (MAE): 0.020827932545358707\n", + "Mean Squared Error (MSE): 0.0006974373791765304\n", + "Root Mean Squared Error (RMSE): 0.02640903972461949\n", + "R-squared (R2): 96.82466595739169\n" + ] + } + ], + "source": [ + "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", + "# Calculate Mean Absolute Error (MAE)\n", + "mae = mean_absolute_error(y_test, final_pred)\n", + "\n", + "# Calculate Mean Squared Error (MSE)\n", + "mse = mean_squared_error(y_test, final_pred)\n", + "\n", + "# Calculate Root Mean Squared Error (RMSE)\n", + "rmse = np.sqrt(mse)\n", + "\n", + "# Calculate R-squared (R2)\n", + "r2 = r2_score(y_test, final_pred)*100\n", + "\n", + "print(\"Mean Absolute Error (MAE):\", mae)\n", + "print(\"Mean Squared Error (MSE):\", mse)\n", + "print(\"Root Mean Squared Error (RMSE):\", rmse)\n", + "print(\"R-squared (R2):\", r2)" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Rainfall_Prediction/Data_handling.ipynb b/Rainfall_Prediction/Data_handling.ipynb new file mode 100644 index 000000000..0edd8c29a --- /dev/null +++ b/Rainfall_Prediction/Data_handling.ipynb @@ -0,0 +1,284 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "V9APVj5zANnm" + }, + "outputs": [], + "source": [ + "import requests\n", + "import numpy as np\n", + "%pip install netCDF4\n", + "\n", + "#importing netCDF file handling module\n", + "from netCDF4 import Dataset\n", + "data=Dataset('/content/drive/MyDrive/RF25_ind2023_rfp25.nc')\n", + "\n", + "# Displaying the names of the variables\n", + "print(data.variables.keys())\n", + "\n", + "# Accessing the variables\n", + "LONGITUDE = data.variables['LONGITUDE']\n", + "print(LONGITUDE)\n", + "\n", + "LATITUDE = data.variables['LATITUDE']\n", + "print(LATITUDE)\n", + "\n", + "time1 = data.variables['TIME']\n", + "print(time1)\n", + "\n", + "precipitation = data.variables['RAINFALL']\n", + "print(precipitation)\n", + "\n", + "# Accessing the data from the variables\n", + "time_data = data.variables['TIME'][:]\n", + "print(time_data)\n", + "\n", + "LONGITUDE_data = data.variables['LONGITUDE'][:]\n", + "print(LONGITUDE_data)\n", + "\n", + "LATITUDE_data = data.variables['LATITUDE'][:]\n", + "print(LATITUDE_data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "L4903BXFlR9n" + }, + "outputs": [], + "source": [ + "#extracting data for particular cordinates\n", + "%pip install pandas\n", + "from netCDF4 import Dataset\n", + "import numpy as np\n", + "import pandas as pd\n", + "for i in range(1980, 2024):\n", + " data=Dataset('/content/drive/MyDrive/RF25_ind'+str(i)+'_rfp25.nc')\n", + " # Storing the LATITUDE and LONGITUDE data into the variables\n", + " LATITUDE = data.variables['LATITUDE'][:]\n", + " LONGITUDE = data.variables['LONGITUDE'][:]\n", + "\n", + " # Storing the LATITUDE and LONGITUDE of into variables\n", + " LATITUDE_dehradun = location.latitude\n", + " LONGITUDE_dehradun = location.longitude\n", + "\n", + " # Squared difference of LATITUDE and LONGITUDE\n", + " sq_diff_LATITUDE = (LATITUDE - LATITUDE_dehradun)**2\n", + " sq_diff_LONGITUDE = (LONGITUDE - LONGITUDE_dehradun)**2\n", + "\n", + " # Identifying the index of the minimum value for LATITUDE and LONGITUDE\n", + " min_index_LATITUDE = sq_diff_LATITUDE.argmin()\n", + " min_index_LONGITUDE = sq_diff_LONGITUDE.argmin()\n", + "\n", + " precipitation= data.variables['RAINFALL']\n", + "\n", + " # Creating an empty pandas dataframe\n", + " starting_date = '1-1-'+data.variables['RAINFALL'].history[8:12]\n", + " print(starting_date)\n", + " ending_date = '31-12-'+data.variables['RAINFALL'].history[8:12]\n", + " print(ending_date)\n", + " date_range = pd.date_range(start = starting_date, end = ending_date)\n", + " df = pd.DataFrame(0, columns = ['precipitation'], index = date_range)\n", + "\n", + " dt = np.arange(0, data.variables['TIME'].size)\n", + "\n", + " for time_index in dt:\n", + " df.iloc[time_index] = precipitation[time_index,min_index_LATITUDE ,min_index_LONGITUDE]\n", + "\n", + " # Saving the time series into a csv\n", + " df.to_csv('temparature_dehradun'+str(i)+'.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "QHsoTmRwsNib" + }, + "outputs": [], + "source": [ + "data = pd.read_csv(\"/content/POWER_Regional_Daily_19810201_19820201_028d4497N_031d1389N_077d3883E_081d3168E_LST.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "YqcIcYKdsLZK", + "outputId": "84dd077d-beec-4b17-c34f-2c74eb04a6ee" + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "data.hist(bins=50, figsize = (20,15))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "K-uhPlMEqOTU" + }, + "outputs": [], + "source": [ + "#plotting data into map\n", + "\n", + "%pip install basemap\n", + "%pip install netCDF4\n", + "from netCDF4 import Dataset\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from mpl_toolkits.basemap import Basemap\n", + "\n", + "data = Dataset('/content/drive/MyDrive/APHRO_MA_025deg_V1901.1998.nc')\n", + "\n", + "LATITUDEs = data.variables['LATITUDE'][:]\n", + "LONGITUDEs = data.variables['LONGITUDE'][:]\n", + "time = data.variables['time'][:]\n", + "precip = data.variables['precip'][:]\n", + "\n", + "mp = Basemap(projection = 'merc',\n", + " llcrnrLONGITUDE = 68.251680,\n", + " llcrnrLATITUDE = 6.487972,\n", + " urcrnrLONGITUDE = 98.085078,\n", + " urcrnrLATITUDE = 34.994298,\n", + " resolution = 'i')\n", + "\n", + "LONGITUDE, LATITUDE = np.meshgrid(LONGITUDEs, LATITUDEs)\n", + "x,y = mp(LONGITUDE, LATITUDE)\n", + "\n", + "c_scheme = mp.pcolor(x, y, np.squeeze(precip[220,:,:]), cmap = 'jet')\n", + "mp.drawcoastlines()\n", + "mp.drawstates()\n", + "mp.drawcountries()\n", + "\n", + "cbar = mp.colorbar(c_scheme, location = 'right', pad = '10%')\n", + "\n", + "plt.title('Average precipitation on 08-08-1998')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "S9ABDeSYBvI9" + }, + "outputs": [], + "source": [ + "#making animation of data\n", + "\n", + "%pip install basemap\n", + "%pip install netCDF4\n", + "from netCDF4 import Dataset\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from mpl_toolkits.basemap import Basemap\n", + "\n", + "data = Dataset('/content/drive/MyDrive/APHRO_MA_025deg_V1901.1998.nc')\n", + "\n", + "LATITUDEs = data.variables['LATITUDE'][:]\n", + "LONGITUDEs = data.variables['LONGITUDE'][:]\n", + "time = data.variables['time'][:]\n", + "precip = data.variables['precip'][:]\n", + "\n", + "mp = Basemap(projection = 'merc',\n", + " llcrnrLONGITUDE = 68.251680,\n", + " llcrnrLATITUDE = 6.487972,\n", + " urcrnrLONGITUDE = 98.085078,\n", + " urcrnrLATITUDE = 34.994298,\n", + " resolution = 'i')\n", + "\n", + "LONGITUDE, LATITUDE = np.meshgrid(LONGITUDEs, LATITUDEs)\n", + "x,y = mp(LONGITUDE, LATITUDE)\n", + "\n", + "days = np.arange(0, 10)\n", + "\n", + "for i in days:\n", + "\n", + " c_scheme = mp.pcolor(x, y, np.squeeze(precip[i,:,:]), cmap = 'jet')\n", + " mp.drawcoastlines()\n", + " mp.drawstates()\n", + " mp.drawcountries()\n", + "\n", + " cbar = mp.colorbar(c_scheme, location = 'right', pad = '10%')\n", + " day = i+1\n", + "\n", + " plt.title('Average Precipitation: Day ' + str(day) + ' of Year 1998' )\n", + " plt.clim(0, 70)\n", + " plt.savefig(r'/content/drive/MyDrive/animation'+'\\\\'+ str(day)+'.jpg')\n", + " plt.clf()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ozTLVdWOIjFW", + "outputId": "b849ae0b-8ddb-4b06-d166-1a0a063b8399" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (9.4.0)\n" + ] + } + ], + "source": [ + "#making gif\n", + "%pip install Pillow\n", + "import PIL\n", + "import numpy as np\n", + "\n", + "image_frames = []\n", + "\n", + "days = np.arange(1,366)\n", + "image_frames = []\n", + "for k in days:\n", + " new_frame = PIL.Image.open('/content/drive/MyDrive/animation/animation\\\\'+str(k)+'.jpg')\n", + " image_frames.append(new_frame)\n", + "image_frames[0].save('animation.gif', format='GIF', save_all=True, append_images=image_frames[1:], duration=150, loop=0)" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Rainfall_Prediction/POWER_Point_Daily.csv b/Rainfall_Prediction/POWER_Point_Daily.csv new file mode 100644 index 000000000..4fcbdd281 --- /dev/null +++ b/Rainfall_Prediction/POWER_Point_Daily.csv @@ -0,0 +1,14336 @@ +YEAR,MO,DY,T2M_MAX,T2M_MIN,TS,RH2M,PRECTOTCORR,WS10M +1982,1,1,14.94,2.58,2.76,35.19,-0.00000001,2.09 +1982,1,2,15.5,2.85,3.21,34.69,-0.00000001,2.22 +1982,1,3,14.44,2.9,2.33,29.31,-0.00000001,1.66 +1982,1,4,7.72,-0.85,2.22,73.31,2.55,2.23 +1982,1,5,10.55,0.2,3.15,69.56,0.08,1.83 +1982,1,6,14.06,-0.95,2.23,41.94,-0.00000001,2.21 +1982,1,7,15.96,2.88,2.98,35,-0.00000001,1.94 +1982,1,8,12.92,4.33,2.27,36.12,-0.00000001,1.45 +1982,1,9,12.65,0.6,1.84,46.56,4.64,1.55 +1982,1,10,6.94,-0.31,0.06,78.62,3.63,1.52 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