diff --git "a/code/2021_autumn/\346\235\250\350\212\203\346\230\225-\345\237\272\344\272\216LSTM\347\232\204\347\251\272\346\260\224\344\272\214\346\254\241\351\242\204\346\212\245/.keep" "b/code/2021_autumn/\346\235\250\350\212\203\346\230\225-\345\237\272\344\272\216LSTM\347\232\204\347\251\272\346\260\224\344\272\214\346\254\241\351\242\204\346\212\245/.keep"
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git "a/code/2021_autumn/\346\235\250\350\212\203\346\230\225-\345\237\272\344\272\216LSTM\347\232\204\347\251\272\346\260\224\344\272\214\346\254\241\351\242\204\346\212\245/AA1A2A3\344\270\200\346\254\241\351\242\204\346\212\245\345\200\274.csv" "b/code/2021_autumn/\346\235\250\350\212\203\346\230\225-\345\237\272\344\272\216LSTM\347\232\204\347\251\272\346\260\224\344\272\214\346\254\241\351\242\204\346\212\245/AA1A2A3\344\270\200\346\254\241\351\242\204\346\212\245\345\200\274.csv"
new file mode 100644
index 0000000000000000000000000000000000000000..7ddc8295d4ea18a6bbd2ec1ad7ad34d08559b54d
--- /dev/null
+++ "b/code/2021_autumn/\346\235\250\350\212\203\346\230\225-\345\237\272\344\272\216LSTM\347\232\204\347\251\272\346\260\224\344\272\214\346\254\241\351\242\204\346\212\245/AA1A2A3\344\270\200\346\254\241\351\242\204\346\212\245\345\200\274.csv"
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diff --git "a/code/2021_autumn/\346\235\250\350\212\203\346\230\225-\345\237\272\344\272\216LSTM\347\232\204\347\251\272\346\260\224\344\272\214\346\254\241\351\242\204\346\212\245/AA1A2A3\345\256\236\351\231\205\350\247\202\346\265\213\345\200\274.csv" "b/code/2021_autumn/\346\235\250\350\212\203\346\230\225-\345\237\272\344\272\216LSTM\347\232\204\347\251\272\346\260\224\344\272\214\346\254\241\351\242\204\346\212\245/AA1A2A3\345\256\236\351\231\205\350\247\202\346\265\213\345\200\274.csv"
new file mode 100644
index 0000000000000000000000000000000000000000..b9ab5da08eeff77af3aaa16aec80facacee60ef8
--- /dev/null
+++ "b/code/2021_autumn/\346\235\250\350\212\203\346\230\225-\345\237\272\344\272\216LSTM\347\232\204\347\251\272\346\260\224\344\272\214\346\254\241\351\242\204\346\212\245/AA1A2A3\345\256\236\351\231\205\350\247\202\346\265\213\345\200\274.csv"
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diff --git "a/code/2021_autumn/\346\235\250\350\212\203\346\230\225-\345\237\272\344\272\216LSTM\347\232\204\347\251\272\346\260\224\344\272\214\346\254\241\351\242\204\346\212\245/A\347\202\271\344\270\200\346\254\241\351\242\204\346\212\245\345\200\274.csv" "b/code/2021_autumn/\346\235\250\350\212\203\346\230\225-\345\237\272\344\272\216LSTM\347\232\204\347\251\272\346\260\224\344\272\214\346\254\241\351\242\204\346\212\245/A\347\202\271\344\270\200\346\254\241\351\242\204\346\212\245\345\200\274.csv"
new file mode 100644
index 0000000000000000000000000000000000000000..394b40c4615a4d58f507808c05e2ee91b761436e
--- /dev/null
+++ "b/code/2021_autumn/\346\235\250\350\212\203\346\230\225-\345\237\272\344\272\216LSTM\347\232\204\347\251\272\346\260\224\344\272\214\346\254\241\351\242\204\346\212\245/A\347\202\271\344\270\200\346\254\241\351\242\204\346\212\245\345\200\274.csv"
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diff --git "a/code/2021_autumn/\346\235\250\350\212\203\346\230\225-\345\237\272\344\272\216LSTM\347\232\204\347\251\272\346\260\224\344\272\214\346\254\241\351\242\204\346\212\245/A\347\202\271\345\206\205\346\217\222\350\247\202\346\265\213\345\200\274.csv" "b/code/2021_autumn/\346\235\250\350\212\203\346\230\225-\345\237\272\344\272\216LSTM\347\232\204\347\251\272\346\260\224\344\272\214\346\254\241\351\242\204\346\212\245/A\347\202\271\345\206\205\346\217\222\350\247\202\346\265\213\345\200\274.csv"
new file mode 100644
index 0000000000000000000000000000000000000000..45fc5483b8987f0b1f401d87e8caad5f11b2096e
--- /dev/null
+++ "b/code/2021_autumn/\346\235\250\350\212\203\346\230\225-\345\237\272\344\272\216LSTM\347\232\204\347\251\272\346\260\224\344\272\214\346\254\241\351\242\204\346\212\245/A\347\202\271\345\206\205\346\217\222\350\247\202\346\265\213\345\200\274.csv"
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diff --git "a/code/2021_autumn/\346\235\250\350\212\203\346\230\225-\345\237\272\344\272\216LSTM\347\232\204\347\251\272\346\260\224\344\272\214\346\254\241\351\242\204\346\212\245/ReadMe.txt" "b/code/2021_autumn/\346\235\250\350\212\203\346\230\225-\345\237\272\344\272\216LSTM\347\232\204\347\251\272\346\260\224\344\272\214\346\254\241\351\242\204\346\212\245/ReadMe.txt"
new file mode 100644
index 0000000000000000000000000000000000000000..28860459e49ece5c8e65e0a85ee5ff578ab0e9c2
--- /dev/null
+++ "b/code/2021_autumn/\346\235\250\350\212\203\346\230\225-\345\237\272\344\272\216LSTM\347\232\204\347\251\272\346\260\224\344\272\214\346\254\241\351\242\204\346\212\245/ReadMe.txt"
@@ -0,0 +1,5 @@
+通过监测站提供的实测数据以及WRF-CMAQ 模型计算得到的一次预报数据，结合提出的二次预报模型得到更准确的空气污染预报值。
+
+weather_pre_LSTM 是单站预报模型
+weather_pre_GEOLSTM 是区域预报模型
+另外四个CSV文件是经过数据预处理后的数据文件
\ No newline at end of file
diff --git "a/code/2021_autumn/\346\235\250\350\212\203\346\230\225-\345\237\272\344\272\216LSTM\347\232\204\347\251\272\346\260\224\344\272\214\346\254\241\351\242\204\346\212\245/one_pre_data.txt" "b/code/2021_autumn/\346\235\250\350\212\203\346\230\225-\345\237\272\344\272\216LSTM\347\232\204\347\251\272\346\260\224\344\272\214\346\254\241\351\242\204\346\212\245/one_pre_data.txt"
new file mode 100644
index 0000000000000000000000000000000000000000..c1652da199cb02f2e4880cdd86d8da26a0140504
--- /dev/null
+++ "b/code/2021_autumn/\346\235\250\350\212\203\346\230\225-\345\237\272\344\272\216LSTM\347\232\204\347\251\272\346\260\224\344\272\214\346\254\241\351\242\204\346\212\245/one_pre_data.txt"
@@ -0,0 +1,257 @@
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+ 4.78553]
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diff --git "a/code/2021_autumn/\346\235\250\350\212\203\346\230\225-\345\237\272\344\272\216LSTM\347\232\204\347\251\272\346\260\224\344\272\214\346\254\241\351\242\204\346\212\245/two_pre_data.txt" "b/code/2021_autumn/\346\235\250\350\212\203\346\230\225-\345\237\272\344\272\216LSTM\347\232\204\347\251\272\346\260\224\344\272\214\346\254\241\351\242\204\346\212\245/two_pre_data.txt"
new file mode 100644
index 0000000000000000000000000000000000000000..0b645a86aa02cad8ce501a857344c1cb41f68b00
--- /dev/null
+++ "b/code/2021_autumn/\346\235\250\350\212\203\346\230\225-\345\237\272\344\272\216LSTM\347\232\204\347\251\272\346\260\224\344\272\214\346\254\241\351\242\204\346\212\245/two_pre_data.txt"
@@ -0,0 +1,313 @@
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diff --git "a/code/2021_autumn/\346\235\250\350\212\203\346\230\225-\345\237\272\344\272\216LSTM\347\232\204\347\251\272\346\260\224\344\272\214\346\254\241\351\242\204\346\212\245/weather_pre _GEOLSTM.ipynb.ipynb" "b/code/2021_autumn/\346\235\250\350\212\203\346\230\225-\345\237\272\344\272\216LSTM\347\232\204\347\251\272\346\260\224\344\272\214\346\254\241\351\242\204\346\212\245/weather_pre _GEOLSTM.ipynb.ipynb"
new file mode 100644
index 0000000000000000000000000000000000000000..a23608f84c6e5546a2a035534da3feaeb3b62acb
--- /dev/null
+++ "b/code/2021_autumn/\346\235\250\350\212\203\346\230\225-\345\237\272\344\272\216LSTM\347\232\204\347\251\272\346\260\224\344\272\214\346\254\241\351\242\204\346\212\245/weather_pre _GEOLSTM.ipynb.ipynb"	
@@ -0,0 +1,332 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "538f539c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 4.00000000e+00  8.00000000e+00  7.00000000e+00 ...  3.62144000e+00\n",
+      "   1.00000000e+00  0.00000000e+00]\n",
+      " [ 4.00000000e+00  8.00000000e+00  7.00000000e+00 ...  2.87348000e+00\n",
+      "   9.65925826e-01  2.58819045e-01]\n",
+      " [ 4.00000000e+00  9.00000000e+00  6.00000000e+00 ...  2.05622000e+00\n",
+      "   8.66025404e-01  5.00000000e-01]\n",
+      " ...\n",
+      " [ 9.00000000e+00  6.00000000e+00  6.00000000e+00 ...  3.08468000e+00\n",
+      "  -5.00000000e-01 -8.66025404e-01]\n",
+      " [ 8.00000000e+00  7.00000000e+00  8.00000000e+00 ...  4.00340000e+00\n",
+      "  -2.58819045e-01 -9.65925826e-01]\n",
+      " [ 8.00000000e+00  7.00000000e+00  5.00000000e+00 ...  4.78553000e+00\n",
+      "  -1.84000000e-16 -1.00000000e+00]]\n",
+      "WARNING:tensorflow:sample_weight modes were coerced from\n",
+      "  ...\n",
+      "    to  \n",
+      "  ['...']\n",
+      "WARNING:tensorflow:sample_weight modes were coerced from\n",
+      "  ...\n",
+      "    to  \n",
+      "  ['...']\n",
+      "Train for 200 steps, validate for 58 steps\n",
+      "Epoch 1/15\n",
+      "200/200 - 5s - loss: 0.5655 - val_loss: 0.5023\n",
+      "Epoch 2/15\n",
+      "200/200 - 2s - loss: 0.4837 - val_loss: 0.5376\n",
+      "Epoch 3/15\n",
+      "200/200 - 2s - loss: 0.4610 - val_loss: 0.5471\n",
+      "Epoch 4/15\n",
+      "200/200 - 2s - loss: 0.4364 - val_loss: 0.6337\n",
+      "Epoch 5/15\n",
+      "200/200 - 2s - loss: 0.4194 - val_loss: 0.6153\n",
+      "Epoch 6/15\n",
+      "200/200 - 2s - loss: 0.4093 - val_loss: 0.5939\n",
+      "Epoch 7/15\n",
+      "200/200 - 2s - loss: 0.3907 - val_loss: 0.6095\n",
+      "Epoch 8/15\n",
+      "200/200 - 2s - loss: 0.3815 - val_loss: 0.6126\n",
+      "Epoch 9/15\n",
+      "200/200 - 2s - loss: 0.3660 - val_loss: 0.5988\n",
+      "Epoch 10/15\n",
+      "200/200 - 2s - loss: 0.3516 - val_loss: 0.6600\n",
+      "Epoch 11/15\n",
+      "200/200 - 2s - loss: 0.3437 - val_loss: 0.6508\n",
+      "Epoch 12/15\n",
+      "200/200 - 2s - loss: 0.3305 - val_loss: 0.5991\n",
+      "Epoch 13/15\n",
+      "200/200 - 2s - loss: 0.3290 - val_loss: 0.6270\n",
+      "Epoch 14/15\n",
+      "200/200 - 2s - loss: 0.3246 - val_loss: 0.6352\n",
+      "Epoch 15/15\n",
+      "200/200 - 2s - loss: 0.3144 - val_loss: 0.6341\n",
+      "[[ 1.00000000e+01  4.00000000e+00  8.00000000e+00 ...  2.78232000e+01\n",
+      "   1.00000000e+00  0.00000000e+00]\n",
+      " [ 1.30000000e+01  6.00000000e+00  7.00000000e+00 ...  2.66929000e+01\n",
+      "   9.65925826e-01  2.58819045e-01]\n",
+      " [ 8.00000000e+00  8.00000000e+00  6.00000000e+00 ...  2.07078000e+01\n",
+      "   8.66025404e-01  5.00000000e-01]\n",
+      " ...\n",
+      " [ 1.90000000e+01  2.10000000e+01  2.20000000e+01 ...  4.15508000e+01\n",
+      "  -5.00000000e-01 -8.66025404e-01]\n",
+      " [ 2.00000000e+01  1.60000000e+01  1.30000000e+01 ...  4.24011000e+01\n",
+      "  -2.58819045e-01 -9.65925826e-01]\n",
+      " [ 1.40000000e+01  1.40000000e+01  1.00000000e+01 ...  4.26422000e+01\n",
+      "  -1.84000000e-16 -1.00000000e+00]]\n",
+      "WARNING:tensorflow:sample_weight modes were coerced from\n",
+      "  ...\n",
+      "    to  \n",
+      "  ['...']\n",
+      "WARNING:tensorflow:sample_weight modes were coerced from\n",
+      "  ...\n",
+      "    to  \n",
+      "  ['...']\n",
+      "Train for 200 steps, validate for 58 steps\n",
+      "Epoch 1/15\n",
+      "200/200 - 4s - loss: 0.6144 - val_loss: 0.3072\n",
+      "Epoch 2/15\n",
+      "200/200 - 2s - loss: 0.5618 - val_loss: 0.3043\n",
+      "Epoch 3/15\n",
+      "200/200 - 2s - loss: 0.5275 - val_loss: 0.3049\n",
+      "Epoch 4/15\n",
+      "200/200 - 2s - loss: 0.5071 - val_loss: 0.2922\n",
+      "Epoch 5/15\n",
+      "200/200 - 2s - loss: 0.4942 - val_loss: 0.2872\n",
+      "Epoch 6/15\n",
+      "200/200 - 2s - loss: 0.4764 - val_loss: 0.2926\n",
+      "Epoch 7/15\n",
+      "200/200 - 2s - loss: 0.4673 - val_loss: 0.2889\n",
+      "Epoch 8/15\n",
+      "200/200 - 2s - loss: 0.4445 - val_loss: 0.2808\n",
+      "Epoch 9/15\n",
+      "200/200 - 2s - loss: 0.4226 - val_loss: 0.2842\n",
+      "Epoch 10/15\n",
+      "200/200 - 2s - loss: 0.4228 - val_loss: 0.2702\n",
+      "Epoch 11/15\n",
+      "200/200 - 2s - loss: 0.4090 - val_loss: 0.2895\n",
+      "Epoch 12/15\n",
+      "200/200 - 2s - loss: 0.3941 - val_loss: 0.2767\n",
+      "Epoch 13/15\n",
+      "200/200 - 2s - loss: 0.3896 - val_loss: 0.2885\n",
+      "Epoch 14/15\n",
+      "200/200 - 2s - loss: 0.3796 - val_loss: 0.2946\n",
+      "Epoch 15/15\n",
+      "200/200 - 2s - loss: 0.3708 - val_loss: 0.2948\n",
+      "[[ 1.70000000e+01  2.80000000e+01  2.50000000e+01 ...  1.42016000e+01\n",
+      "   1.00000000e+00  0.00000000e+00]\n",
+      " [ 2.90000000e+01  3.30000000e+01  2.80000000e+01 ...  1.88849000e+01\n",
+      "   9.65925826e-01  2.58819045e-01]\n",
+      " [ 2.80000000e+01  3.00000000e+01  3.20000000e+01 ...  1.15324000e+01\n",
+      "   8.66025404e-01  5.00000000e-01]\n",
+      " ...\n",
+      " [ 9.00000000e+00  3.00000000e+01  1.60000000e+01 ...  9.13525000e+00\n",
+      "  -5.00000000e-01 -8.66025404e-01]\n",
+      " [ 2.20000000e+01  3.00000000e+01  2.00000000e+01 ...  8.24599000e+00\n",
+      "  -2.58819045e-01 -9.65925826e-01]\n",
+      " [ 2.60000000e+01  2.20000000e+01  1.30000000e+01 ...  7.40284000e+00\n",
+      "  -1.84000000e-16 -1.00000000e+00]]\n",
+      "WARNING:tensorflow:sample_weight modes were coerced from\n",
+      "  ...\n",
+      "    to  \n",
+      "  ['...']\n",
+      "WARNING:tensorflow:sample_weight modes were coerced from\n",
+      "  ...\n",
+      "    to  \n",
+      "  ['...']\n",
+      "Train for 200 steps, validate for 58 steps\n",
+      "Epoch 1/15\n",
+      "200/200 - 4s - loss: 0.6179 - val_loss: 0.3306\n",
+      "Epoch 2/15\n",
+      "200/200 - 2s - loss: 0.5611 - val_loss: 0.3237\n",
+      "Epoch 3/15\n",
+      "200/200 - 2s - loss: 0.5446 - val_loss: 0.3147\n",
+      "Epoch 4/15\n",
+      "200/200 - 2s - loss: 0.5293 - val_loss: 0.2993\n",
+      "Epoch 5/15\n",
+      "200/200 - 2s - loss: 0.5229 - val_loss: 0.3195\n",
+      "Epoch 6/15\n",
+      "200/200 - 2s - loss: 0.5075 - val_loss: 0.2957\n",
+      "Epoch 7/15\n"
+     ]
+    }
+   ],
+   "source": [
+    "import tensorflow as tf\n",
+    "gpu = tf.config.experimental.list_physical_devices(device_type='GPU')\n",
+    "assert len(gpu) == 1\n",
+    "tf.config.experimental.set_memory_growth(gpu[0], True)\n",
+    "from tensorflow import keras\n",
+    "import numpy as np\n",
+    "import csv\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras import layers\n",
+    "from tensorflow.keras.optimizers import RMSprop\n",
+    "\n",
+    "pollutant = {0:'SO2', 1:'NO2', 2:'PM10', 3:'PM2.5', 4:'O3', 5:'CO'}\n",
+    "def SpiralExplosion(pollutant_type, site_name):\n",
+    "    real_csv = 'AA1A2A3实际观测值.csv'\n",
+    "    pre_csv = 'AA1A2A3一次预报值.csv'\n",
+    "    \n",
+    "    with open(real_csv) as f:\n",
+    "        data = csv.reader(f, delimiter=\",\")\n",
+    "        real_data = []\n",
+    "        for line in data:\n",
+    "            line = list(map(float,line))\n",
+    "            real_data.append((line))\n",
+    "    real_data = np.array(real_data)\n",
+    "    #print(real_data)\n",
+    "    \n",
+    "    with open(pre_csv) as f:\n",
+    "        data = csv.reader(f, delimiter=\",\")\n",
+    "        pre_data = []\n",
+    "        for line in data:\n",
+    "            line = list(map(float,line))\n",
+    "            pre_data.append((line))\n",
+    "    pre_data = np.array(pre_data)\n",
+    "    \n",
+    "    pre_num = 0\n",
+    "    \n",
+    "    pre_all = np.hstack((real_data[:,[pollutant_type,pollutant_type+6,pollutant_type+12,pollutant_type+18]],pre_data[:,[pollutant_type+6*site_name,24,25]]))\n",
+    "    #针对O3特殊处理\n",
+    "    #pre_all = np.hstack((pre_all,real_data[:,1].reshape(len(pre_all),1)))\n",
+    "    print(pre_all)\n",
+    "    #两年数据作为训练集\n",
+    "    data_mean = pre_all.mean(axis=0)\n",
+    "    data_std = pre_all.std(axis=0)\n",
+    "    deal_data = (pre_all - data_mean) / data_std\n",
+    "    \n",
+    "    # 数据生成器编写\n",
+    "    # data ：浮点数据原始数组\n",
+    "    # lookback ：输入数据包含的时间步\n",
+    "    # delay ：目标在多少个时间步之后\n",
+    "    # min_index，max_index ：data数组的索引，用于界定需要抽取哪些时间步。保存一部分用于验证，一部分用于测试\n",
+    "    # shuffle ：决定是否打乱样本；TRUE：打乱 ；FLASE：按顺序处理样本\n",
+    "    # batch_size ：每个批量的样本数\n",
+    "    def generator(data, lookback, delay, min_index, max_index, target_num=0,\n",
+    "                  shuffle=False, batch_size=64):\n",
+    "        if max_index is None:\n",
+    "            max_index = len(data) - delay - 1\n",
+    "        i = min_index + lookback\n",
+    "        while 1:\n",
+    "            if shuffle:\n",
+    "                rows = np.random.randint(\n",
+    "                    min_index + lookback, max_index, size=batch_size)\n",
+    "            else:\n",
+    "                if i + batch_size >= max_index:\n",
+    "                    i = min_index + lookback\n",
+    "                rows = np.arange(i, min(i + batch_size, max_index))\n",
+    "                i += len(rows)\n",
+    "    \n",
+    "            samples = np.zeros((len(rows),\n",
+    "                               lookback,\n",
+    "                               data.shape[-1]))\n",
+    "            targets = np.zeros((len(rows),delay))\n",
+    "            \n",
+    "            for j, row in enumerate(rows):\n",
+    "                    indices_sam = range(rows[j] - lookback, rows[j])\n",
+    "                    indices_tar = range(rows[j] + 1, rows[j] + 1 + delay)\n",
+    "                    samples[j] = data[indices_sam]\n",
+    "                    targets[j] = data[indices_tar][:,site_name]\n",
+    "            yield samples, targets\n",
+    "            \n",
+    "    lookback = 64\n",
+    "    delay = 64\n",
+    "    batch_size = 16\n",
+    "    \n",
+    "    train_gen = generator(deal_data,\n",
+    "                          lookback=lookback,\n",
+    "                          delay=delay,\n",
+    "                          min_index=0,\n",
+    "                          max_index=7001,\n",
+    "                          shuffle=True,\n",
+    "                          batch_size=batch_size)\n",
+    "    val_gen = generator(deal_data,\n",
+    "                        lookback=lookback,\n",
+    "                        delay=delay,\n",
+    "                        min_index=7001,\n",
+    "                        max_index=8001,\n",
+    "                        batch_size=batch_size)\n",
+    "    test_gen = generator(deal_data,\n",
+    "                         lookback=lookback,\n",
+    "                         delay=delay,\n",
+    "                         min_index=8001,\n",
+    "                         max_index=None,\n",
+    "                         batch_size=batch_size)\n",
+    "    \n",
+    "    # This is how many steps to draw from `val_gen`\n",
+    "    # in order to see the whole validation set:\n",
+    "    val_steps = (1000 - lookback) // batch_size\n",
+    "    \n",
+    "    # This is how many steps to draw from `test_gen`\n",
+    "    # in order to see the whole test set:\n",
+    "    test_steps = (len(deal_data) - 8001 - lookback) // batch_size\n",
+    "    \n",
+    "    \n",
+    "    model = Sequential()\n",
+    "    model.add(layers.LSTM(64,\n",
+    "                return_sequences = True,\n",
+    "                input_shape = (None,deal_data.shape[-1])))\n",
+    "                #input_shape = (None,1)))\n",
+    "    model.add(layers.LSTM(32))\n",
+    "    model.add(layers.Dense(64))\n",
+    "    \n",
+    "    model.compile(optimizer=RMSprop(), loss='mae')\n",
+    "    history = model.fit(train_gen,\n",
+    "                        steps_per_epoch=200,\n",
+    "                        epochs=15,\n",
+    "                        validation_data=val_gen,\n",
+    "                        validation_steps=val_steps,\n",
+    "                        verbose = 2)\n",
+    "    \n",
+    "    test_data = deal_data[-64:,].reshape(1,64,7)\n",
+    "    results=model.predict(test_data, batch_size=1)\n",
+    "    ypx = results * data_std[site_name] + data_mean[site_name]\n",
+    "    \n",
+    "    f_one = 'one_pre_data.txt'\n",
+    "    f_two = 'two_pre_data.txt'\n",
+    "    \n",
+    "    with open(f_one,\"a+\") as file:   #追加内容\n",
+    "        file.write(pollutant[pollutant_type]+str(site_name)+':\\n'+str(pre_all[-64:,4])+\"\\n\")\n",
+    "    with open(f_two,\"a+\") as file:   #追加内容\n",
+    "        file.write(pollutant[pollutant_type]+str(site_name)+':\\n'+str(ypx)+\"\\n\")\n",
+    "        \n",
+    "for site_name in range(4):\n",
+    "    for pollutant_type in range(6):\n",
+    "        SpiralExplosion(pollutant_type, site_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a90a1337",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "TensorFlow-2.1",
+   "language": "python",
+   "name": "tensorflow-2.1"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git "a/code/2021_autumn/\346\235\250\350\212\203\346\230\225-\345\237\272\344\272\216LSTM\347\232\204\347\251\272\346\260\224\344\272\214\346\254\241\351\242\204\346\212\245/weather_pre_LSTM.ipynb" "b/code/2021_autumn/\346\235\250\350\212\203\346\230\225-\345\237\272\344\272\216LSTM\347\232\204\347\251\272\346\260\224\344\272\214\346\254\241\351\242\204\346\212\245/weather_pre_LSTM.ipynb"
new file mode 100644
index 0000000000000000000000000000000000000000..04c05c62ea6bff64583ab6ff4d6b4940e8182acd
--- /dev/null
+++ "b/code/2021_autumn/\346\235\250\350\212\203\346\230\225-\345\237\272\344\272\216LSTM\347\232\204\347\251\272\346\260\224\344\272\214\346\254\241\351\242\204\346\212\245/weather_pre_LSTM.ipynb"
@@ -0,0 +1,485 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'2.2.4-tf'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from tensorflow import keras\n",
+    "keras.__version__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 9.00000000e+00  1.20000000e+01  1.00000000e+01 ...  1.60000000e+00\n",
+      "  -5.00000000e-01  8.66025404e-01]\n",
+      " [ 7.00000000e+00  1.40000000e+01  2.40000000e+01 ...  1.40000000e+00\n",
+      "  -7.07106781e-01  7.07106781e-01]\n",
+      " [ 5.00000000e+00  1.30000000e+01  3.80000000e+01 ...  1.50000000e+00\n",
+      "  -8.66025404e-01  5.00000000e-01]\n",
+      " ...\n",
+      " [ 9.00000000e+00  1.90000000e+01  9.00000000e+00 ...  1.00000000e+00\n",
+      "   2.58819045e-01  9.65925826e-01]\n",
+      " [ 8.00000000e+00  2.00000000e+01  2.20000000e+01 ...  5.00000000e-01\n",
+      "   6.12000000e-17  1.00000000e+00]\n",
+      " [ 8.00000000e+00  1.40000000e+01  2.60000000e+01 ...  1.20000000e+00\n",
+      "  -2.58819045e-01  9.65925826e-01]]\n",
+      "[[2.40151e+00 2.09208e+01 8.17336e+00 5.27729e+00 8.78723e+00 1.24491e-01]\n",
+      " [1.44834e+00 1.48144e+01 6.49054e+00 4.33106e+00 1.27453e+01 1.09056e-01]\n",
+      " [1.27161e+00 1.39154e+01 6.86679e+00 4.40045e+00 1.22296e+01 1.05957e-01]\n",
+      " ...\n",
+      " [3.08468e+00 4.15508e+01 9.13525e+00 5.76356e+00 2.47941e-02 2.37719e-01]\n",
+      " [4.00340e+00 4.24011e+01 8.24599e+00 5.43220e+00 9.27702e-04 2.60096e-01]\n",
+      " [4.78553e+00 4.26422e+01 7.40284e+00 5.14862e+00 1.01321e-03 2.68417e-01]]\n",
+      "[[ 2.20000000e+01  8.78723000e+00 -5.00000000e-01  8.66025404e-01\n",
+      "   1.20000000e+01]\n",
+      " [ 3.00000000e+01  1.27453000e+01 -7.07106781e-01  7.07106781e-01\n",
+      "   1.40000000e+01]\n",
+      " [ 4.20000000e+01  1.22296000e+01 -8.66025404e-01  5.00000000e-01\n",
+      "   1.30000000e+01]\n",
+      " ...\n",
+      " [ 8.00000000e+00  2.47941000e-02  2.58819045e-01  9.65925826e-01\n",
+      "   1.90000000e+01]\n",
+      " [ 6.00000000e+00  9.27702000e-04  6.12000000e-17  1.00000000e+00\n",
+      "   2.00000000e+01]\n",
+      " [ 1.90000000e+01  1.01321000e-03 -2.58819045e-01  9.65925826e-01\n",
+      "   1.40000000e+01]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import csv\n",
+    "\n",
+    "real_csv = 'A点内插观测值.csv'\n",
+    "pre_csv = 'A点一次预报值.csv'\n",
+    "\n",
+    "with open(real_csv) as f:\n",
+    "    data = csv.reader(f, delimiter=\",\")\n",
+    "    real_data = []\n",
+    "    for line in data:\n",
+    "        line = list(map(float,line))\n",
+    "        real_data.append((line))\n",
+    "real_data = np.array(real_data)\n",
+    "print(real_data)\n",
+    "\n",
+    "with open(pre_csv) as f:\n",
+    "    data = csv.reader(f, delimiter=\",\")\n",
+    "    pre_data = []\n",
+    "    for line in data:\n",
+    "        line = list(map(float,line))\n",
+    "        pre_data.append((line))\n",
+    "pre_data = np.array(pre_data)\n",
+    "print(pre_data)\n",
+    "\n",
+    "pre_all_one = np.vstack((real_data[:,4],pre_data[:,4]))\n",
+    "pre_all = np.hstack((pre_all_one.transpose(),real_data[:,10:12]))\n",
+    "#针对O3特殊处理\n",
+    "pre_all = np.hstack((pre_all,real_data[:,1].reshape(len(pre_all),1)))\n",
+    "\n",
+    "print(pre_all)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[5.32017233e+01 3.31407075e+01 3.27615387e-20 1.31046155e-20\n",
+      " 3.28726393e+01] [48.36840281 36.35846064  0.70710678  0.70710678 23.68230768]\n",
+      "[[-6.45084839e-01 -6.69815968e-01 -7.07106781e-01  1.22474487e+00\n",
+      "  -8.81360025e-01]\n",
+      " [-4.79687605e-01 -5.60953548e-01 -1.00000000e+00  1.00000000e+00\n",
+      "  -7.96908795e-01]\n",
+      " [-2.31591756e-01 -5.75137317e-01 -1.22474487e+00  7.07106781e-01\n",
+      "  -8.39134410e-01]\n",
+      " ...\n",
+      " [-9.34529997e-01 -9.10817257e-01  3.66025404e-01  1.36602540e+00\n",
+      "  -5.85780722e-01]\n",
+      " [-9.75879305e-01 -9.11473677e-01  8.65035382e-17  1.41421356e+00\n",
+      "  -5.43555107e-01]\n",
+      " [-7.07108801e-01 -9.11471325e-01 -3.66025404e-01  1.36602540e+00\n",
+      "  -7.96908795e-01]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_mean = pre_all.mean(axis=0)\n",
+    "data_std = pre_all.std(axis=0)\n",
+    "\n",
+    "deal_data = (pre_all - data_mean) / data_std\n",
+    "print(data_mean, data_std)\n",
+    "print(deal_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 数据生成器编写\n",
+    "# data ：浮点数据原始数组\n",
+    "# lookback ：输入数据包含的时间步\n",
+    "# delay ：目标在多少个时间步之后\n",
+    "# min_index，max_index ：data数组的索引，用于界定需要抽取哪些时间步。保存一部分用于验证，一部分用于测试\n",
+    "# shuffle ：决定是否打乱样本；TRUE：打乱 ；FLASE：按顺序处理样本\n",
+    "# batch_size ：每个批量的样本数\n",
+    "def generator(data, lookback, delay, min_index, max_index, target_num=0,\n",
+    "              shuffle=False, batch_size=64):\n",
+    "    if max_index is None:\n",
+    "        max_index = len(data) - delay - 1\n",
+    "    i = min_index + lookback\n",
+    "    while 1:\n",
+    "        if shuffle:\n",
+    "            rows = np.random.randint(\n",
+    "                min_index + lookback, max_index, size=batch_size)\n",
+    "        else:\n",
+    "            if i + batch_size >= max_index:\n",
+    "                i = min_index + lookback\n",
+    "            rows = np.arange(i, min(i + batch_size, max_index))\n",
+    "            i += len(rows)\n",
+    "\n",
+    "        samples = np.zeros((len(rows),\n",
+    "                           lookback,\n",
+    "                           data.shape[-1]))\n",
+    "        targets = np.zeros((len(rows),delay))\n",
+    "        \n",
+    "        for j, row in enumerate(rows):\n",
+    "                indices_sam = range(rows[j] - lookback, rows[j])\n",
+    "                indices_tar = range(rows[j] + 1, rows[j] + 1 + delay)\n",
+    "                samples[j] = data[indices_sam]\n",
+    "                targets[j] = data[indices_tar][:,0]\n",
+    "        yield samples, targets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lookback = 64\n",
+    "delay = 64\n",
+    "batch_size = 16\n",
+    "\n",
+    "train_gen = generator(deal_data,\n",
+    "                      lookback=lookback,\n",
+    "                      delay=delay,\n",
+    "                      min_index=0,\n",
+    "                      max_index=7001,\n",
+    "                      shuffle=True,\n",
+    "                      batch_size=batch_size)\n",
+    "val_gen = generator(deal_data,\n",
+    "                    lookback=lookback,\n",
+    "                    delay=delay,\n",
+    "                    min_index=7001,\n",
+    "                    max_index=8001,\n",
+    "                    batch_size=batch_size)\n",
+    "test_gen = generator(deal_data,\n",
+    "                     lookback=lookback,\n",
+    "                     delay=delay,\n",
+    "                     min_index=8001,\n",
+    "                     max_index=None,\n",
+    "                     batch_size=batch_size)\n",
+    "\n",
+    "# This is how many steps to draw from `val_gen`\n",
+    "# in order to see the whole validation set:\n",
+    "val_steps = (1000 - lookback) // batch_size\n",
+    "\n",
+    "# This is how many steps to draw from `test_gen`\n",
+    "# in order to see the whole test set:\n",
+    "test_steps = (len(deal_data) - 8001 - lookback) // batch_size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:sample_weight modes were coerced from\n",
+      "  ...\n",
+      "    to  \n",
+      "  ['...']\n",
+      "WARNING:tensorflow:sample_weight modes were coerced from\n",
+      "  ...\n",
+      "    to  \n",
+      "  ['...']\n",
+      "Train for 200 steps, validate for 58 steps\n",
+      "Epoch 1/15\n",
+      "200/200 [==============================] - 11s 54ms/step - loss: 0.5896 - val_loss: 0.3824\n",
+      "Epoch 2/15\n",
+      "200/200 [==============================] - 1s 5ms/step - loss: 0.5100 - val_loss: 0.3759\n",
+      "Epoch 3/15\n",
+      "200/200 [==============================] - 1s 5ms/step - loss: 0.4987 - val_loss: 0.3655\n",
+      "Epoch 4/15\n",
+      "200/200 [==============================] - 1s 5ms/step - loss: 0.4839 - val_loss: 0.3707\n",
+      "Epoch 5/15\n",
+      "200/200 [==============================] - 1s 5ms/step - loss: 0.4735 - val_loss: 0.3528\n",
+      "Epoch 6/15\n",
+      "200/200 [==============================] - 1s 5ms/step - loss: 0.4672 - val_loss: 0.3729\n",
+      "Epoch 7/15\n",
+      "200/200 [==============================] - 1s 5ms/step - loss: 0.4549 - val_loss: 0.3483\n",
+      "Epoch 8/15\n",
+      "200/200 [==============================] - 1s 5ms/step - loss: 0.4533 - val_loss: 0.3698\n",
+      "Epoch 9/15\n",
+      "200/200 [==============================] - 1s 5ms/step - loss: 0.4404 - val_loss: 0.3641\n",
+      "Epoch 10/15\n",
+      "200/200 [==============================] - 1s 6ms/step - loss: 0.4351 - val_loss: 0.3718\n",
+      "Epoch 11/15\n",
+      "200/200 [==============================] - 1s 5ms/step - loss: 0.4252 - val_loss: 0.3716\n",
+      "Epoch 12/15\n",
+      "200/200 [==============================] - 1s 5ms/step - loss: 0.4181 - val_loss: 0.3649\n",
+      "Epoch 13/15\n",
+      "200/200 [==============================] - 1s 5ms/step - loss: 0.4075 - val_loss: 0.3881\n",
+      "Epoch 14/15\n",
+      "200/200 [==============================] - 1s 5ms/step - loss: 0.4018 - val_loss: 0.3874\n",
+      "Epoch 15/15\n",
+      "200/200 [==============================] - 1s 5ms/step - loss: 0.3937 - val_loss: 0.3963\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras import layers\n",
+    "from tensorflow.keras.optimizers import RMSprop\n",
+    "import numpy as np\n",
+    "\n",
+    "model = Sequential()\n",
+    "model.add(layers.LSTM(64,\n",
+    "            #return_sequences = True,\n",
+    "            input_shape = (None,deal_data.shape[-1])))\n",
+    "            #input_shape = (None,1)))\n",
+    "#model.add(layers.LSTM(32))\n",
+    "model.add(layers.Dense(64))\n",
+    "\n",
+    "model.compile(optimizer=RMSprop(), loss='mae')\n",
+    "history = model.fit(train_gen,\n",
+    "                    steps_per_epoch=200,\n",
+    "                    epochs=15,\n",
+    "                    validation_data=val_gen,\n",
+    "                    validation_steps=val_steps,\n",
+    "                    verbose = 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "loss = history.history['loss']\n",
+    "val_loss = history.history['val_loss']\n",
+    "\n",
+    "epochs = range(len(loss))\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.subplots(figsize=(20,10))\n",
+    "plt.plot(epochs, loss, 'ro', label='Training loss')\n",
+    "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
+    "plt.title('Training and validation loss')\n",
+    "plt.legend(fontsize=24)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(400, 64)\n",
+      "(384,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "results = model.predict(test_gen, steps=test_steps,)\n",
+    "ypx = []\n",
+    "print(results.shape)\n",
+    "for i in range(int(len(results)/64)):\n",
+    "    pre_one_day = results[i*64] * data_std[0] + data_mean[0]\n",
+    "    ypx = np.append(ypx,pre_one_day)\n",
+    "\n",
+    "print(ypx.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1500x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib\n",
+    "#设置标签格式\n",
+    "matplotlib.rcParams['xtick.labelsize'] = 18\n",
+    "matplotlib.rcParams['ytick.labelsize'] = 18\n",
+    "# X、Y轴刻度标签字体大小\n",
+    "matplotlib.rcParams['axes.titlesize'] = 24\n",
+    "matplotlib.rcParams['axes.labelsize'] = 24\n",
+    "fig, ax = plt.subplots(figsize=(15,6),)\n",
+    "plt.ylim(0,200)\n",
+    "#plt.plot(pre_all[8001:,1] , 'o', color='r', label='Provide_pre', markersize=5)\n",
+    "plt.plot(ypx, 'o', color='r', label='LSTM_pre', markersize=5)\n",
+    "plt.plot(real_data[8001+64:,4], '^', color='b', label='Real_data', markersize=6)\n",
+    "plt.legend(fontsize=20)\n",
+    "plt.xlabel('Epoch(1h)')\n",
+    "plt.ylabel('PM2.5(μg/m³)')\n",
+    "plt.savefig('PM2.5.jpg', dpi=300)#保存图片\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 25.280148  41.258316  61.047783  75.624214  89.07788  104.42602\n",
+      "  107.25432  103.07933   90.05765   72.17066   61.20095   44.940067\n",
+      "   37.911724  33.71205   34.041313  34.30652   31.367296  30.822739\n",
+      "   29.748415  24.344429  22.30865   18.588211  17.408161  18.713947\n",
+      "   29.196234  43.300816  65.2846    81.72275   99.73979  102.810745\n",
+      "  106.10472  106.43534   91.75273   79.112465  61.315025  47.396088\n",
+      "   41.636772  38.19194   38.09062   34.015762  35.06066   30.769663\n",
+      "   31.117958  23.031582  25.244225  18.48079   22.775257  23.825275\n",
+      "   33.18247   50.903496  65.47842   87.56383   97.813126  99.79358\n",
+      "  108.31768   95.08885   79.92638   66.713196  45.50233   37.64712\n",
+      "   32.005737  25.287376  23.125864  27.712532]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_data = deal_data[-64:,].reshape(1,64,5)\n",
+    "results=model.predict(test_data, batch_size=1)\n",
+    "ypx = results * data_std[0] + data_mean[0]\n",
+    "print(ypx)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib\n",
+    "#设置标签格式\n",
+    "matplotlib.rcParams['xtick.labelsize'] = 18\n",
+    "matplotlib.rcParams['ytick.labelsize'] = 18\n",
+    "# X、Y轴刻度标签字体大小\n",
+    "matplotlib.rcParams['axes.titlesize'] = 24\n",
+    "matplotlib.rcParams['axes.labelsize'] = 24\n",
+    "fig, ax = plt.subplots(figsize=(10,6),)\n",
+    "plt.ylim(0,200)\n",
+    "plt.plot(pre_all[-64:,1] , 'o', color='r', label='Original_pre', markersize=8)\n",
+    "plt.plot(ypx.reshape(64), 'o', color='b', label='LSTM_pre', markersize=8)\n",
+    "plt.legend(fontsize=20)\n",
+    "plt.xlabel('Epoch(1h)')\n",
+    "plt.ylabel('PM2.5(μg/m³)')\n",
+    "plt.savefig('PM2.5.jpg', dpi=300)#保存图片\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "TensorFlow-2.1",
+   "language": "python",
+   "name": "tensorflow-2.1"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}