cnn bilstm气温预测python
时间: 2025-03-06 11:42:29 浏览: 68
### 使用CNN和BiLSTM进行气温预测的Python实现
#### 数据准备
为了构建有效的气温预测模型,获取高质量的数据集至关重要[^1]。通常情况下,气象数据可以从公开数据库下载,如NOAA、中国气象局等官方渠道。
```python
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
# 假设已有一个CSV文件包含了历史温度记录和其他相关特征
data = pd.read_csv('temperature_data.csv')
# 对时间序列做预处理,比如填充缺失值、去除异常点等操作
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_data = scaler.fit_transform(data)
X_train, y_train, X_test, y_test = train_test_split(scaled_data[:, :-1], scaled_data[:, -1:], test_size=0.2)
```
#### 构建CNN层
由于气温变化存在空间上的关联特性,在输入端采用卷积神经网络可以有效捕捉这些模式。这里定义了一个简单的二维卷积层来提取局部的空间特征。
```python
import torch.nn as nn
import torch
class CNNEncoder(nn.Module):
def __init__(self, input_channels, output_dim):
super(CNNEncoder, self).__init__()
self.conv_layer = nn.Sequential(
nn.Conv2d(in_channels=input_channels,
out_channels=32,
kernel_size=(3, 3),
stride=(1, 1)),
nn.ReLU(),
nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2)))
def forward(self, x):
conv_out = self.conv_layer(x)
flattened = torch.flatten(conv_out, start_dim=1)
return flattened
```
#### 添加BiLSTM层
考虑到气温随时间的变化趋势,后续加入双向长短时记忆单元(Bidirectional Long Short-Term Memory),即BiLSTM,用于处理经过CNN编码后的特征向量,并从中学习到长期依赖关系。
```python
class BiLSTMLayer(nn.Module):
def __init__(self, hidden_units, num_layers, dropout_rate):
super(BiLSTMLayer, self).__init__()
self.bi_lstm = nn.LSTM(input_size=hidden_units * 49,
hidden_size=hidden_units,
num_layers=num_layers,
batch_first=True,
bidirectional=True,
dropout=dropout_rate if num_layers > 1 else 0.)
def forward(self, x):
lstm_output, _ = self.bi_lstm(x)
return lstm_output
```
#### 完整模型架构组合
最后将上述两部分结合起来形成完整的预测框架:
```python
class TemperaturePredictionModel(nn.Module):
def __init__(self, cnn_input_channels, bilstm_hidden_units, bilstm_num_layers, dropout_rate):
super(TemperaturePredictionModel, self).__init__()
self.cnn_encoder = CNNEncoder(cnn_input_channels, bilstm_hidden_units*49)
self.bilstm_layer = BiLSTMLayer(bilstm_hidden_units=bilstm_hidden_units,
num_layers=bilstm_num_layers,
dropout_rate=dropout_rate)
self.fc = nn.Linear(2*bilstm_hidden_units, 1) # 双向所以乘以2
def forward(self, inputs):
encoded_features = []
for i in range(inputs.shape[1]):
feature_map = self.cnn_encoder(inputs[:, i:i+1])
encoded_features.append(feature_map.unsqueeze(dim=1))
sequence_of_encoded_features = torch.cat(encoded_features, dim=1)
bi_lstm_outputs = self.bilstm_layer(sequence_of_encoded_features)
predictions = self.fc(bi_lstm_outputs).squeeze(-1)
return predictions
```
#### 训练过程
完成以上步骤之后就可以开始训练这个混合型深度学习模型了。需要注意的是实际应用中还需要考虑更多细节问题,例如超参数调整、正则化手段以及防止过拟合等问题。
```python
model = TemperaturePredictionModel(...)
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
for epoch in range(num_epochs):
model.train()
outputs = model(X_train_tensor)
loss = criterion(outputs, y_train_tensor)
optimizer.zero_grad()
loss.backward()
optimizer.step()
```
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