lstm python 代码实现降水预测

以下是一个基于LSTM的降水预测模型的Python代码实现示例：

首先，导入必要的库：

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import Dense, LSTM

接着，读取数据集并进行预处理：

# 读取数据集
data = pd.read_csv('rainfall_data.csv')
# 按日期排序
data = data.sort_values('Date')
# 取出降水量列
rainfall = data['Rainfall'].values.reshape(-1, 1)

# 将数据集划分为训练集和测试集
train_size = int(len(rainfall) * 0.8)
test_size = len(rainfall) - train_size
train_data, test_data = rainfall[0:train_size,:], rainfall[train_size:len(rainfall),:]

# 归一化数据
scaler = MinMaxScaler(feature_range=(0, 1))
train_data = scaler.fit_transform(train_data)
test_data = scaler.transform(test_data)

# 将数据集转换为有监督学习问题
def create_dataset(dataset, look_back=1):
    dataX, dataY = [], []
    for i in range(len(dataset)-look_back-1):
        a = dataset[i:(i+look_back), 0]
        dataX.append(a)
        dataY.append(dataset[i + look_back, 0])
    return np.array(dataX), np.array(dataY)

look_back = 10
trainX, trainY = create_dataset(train_data, look_back)
testX, testY = create_dataset(test_data, look_back)

# 将数据集重塑为LSTM输入格式
trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))
testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1]))

然后，构建LSTM模型并进行训练：

model = Sequential()
model.add(LSTM(4, input_shape=(1, look_back)))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, epochs=100, batch_size=1, verbose=2)

最后，使用训练好的模型进行预测并绘制预测结果：

# 在训练集和测试集上进行预测
trainPredict = model.predict(trainX)
testPredict = model.predict(testX)

# 将预测结果逆归一化
trainPredict = scaler.inverse_transform(trainPredict)
trainY = scaler.inverse_transform([trainY])
testPredict = scaler.inverse_transform(testPredict)
testY = scaler.inverse_transform([testY])

# 计算训练集和测试集上的均方根误差
trainScore = np.sqrt(mean_squared_error(trainY[0], trainPredict[:,0]))
print('Train Score: %.2f RMSE' % (trainScore))
testScore = np.sqrt(mean_squared_error(testY[0], testPredict[:,0]))
print('Test Score: %.2f RMSE' % (testScore))

# 绘制预测结果
trainPredictPlot = np.empty_like(rainfall)
trainPredictPlot[:, :] = np.nan
trainPredictPlot[look_back:len(trainPredict)+look_back, :] = trainPredict
testPredictPlot = np.empty_like(rainfall)
testPredictPlot[:, :] = np.nan
testPredictPlot[len(trainPredict)+(look_back*2)+1:len(rainfall)-1, :] = testPredict
plt.plot(scaler.inverse_transform(rainfall))
plt.plot(trainPredictPlot)
plt.plot(testPredictPlot)
plt.show()

此代码实现了一个基于LSTM的降水预测模型，可以通过修改数据集、调整模型参数等进行进一步的改进。