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使用Python和Transformer模型进行土壤水热模拟与预测的详细步骤和示例代码

article 2025/3/6 19:26:10

以下是一个使用Python和Transformer模型进行土壤水热模拟与预测的详细步骤和示例代码。

1. 环境准备

首先，你需要安装必要的库，如torch用于深度学习，numpy用于数值计算，pandas用于数据处理等。可以使用以下命令进行安装：

pip install torch numpy pandas

2. 数据准备

假设你已经有了土壤水热相关的数据，数据包含特征（如温度、湿度等）和目标值（如土壤含水量、土壤温度等）。以下是一个简单的数据加载和预处理示例：

import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
from torch.utils.data import Dataset, DataLoader

# 加载数据
data = pd.read_csv('soil_data.csv')

# 分离特征和目标值
features = data.drop(['target_variable'], axis=1).values
targets = data['target_variable'].values

# 数据标准化
scaler_features = StandardScaler()
scaler_targets = StandardScaler()
features = scaler_features.fit_transform(features)
targets = scaler_targets.fit_transform(targets.reshape(-1, 1)).flatten()

# 划分训练集和测试集
train_size = int(len(features) * 0.8)
train_features, test_features = features[:train_size], features[train_size:]
train_targets, test_targets = targets[:train_size], targets[train_size:]

# 自定义数据集类
class SoilDataset(Dataset):
    def __init__(self, features, targets):
        self.features = features
        self.targets = targets

    def __len__(self):
        return len(self.features)

    def __getitem__(self, idx):
        feature = self.features[idx]
        target = self.targets[idx]
        return feature, target

# 创建数据加载器
train_dataset = SoilDataset(train_features, train_targets)
test_dataset = SoilDataset(test_features, test_targets)
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)

3. 构建Transformer模型

以下是一个简单的Transformer模型示例：

import torch
import torch.nn as nn

class TransformerModel(nn.Module):
    def __init__(self, input_dim, d_model, nhead, num_layers, output_dim):
        super(TransformerModel, self).__init__()
        self.embedding = nn.Linear(input_dim, d_model)
        self.transformer_encoder = nn.TransformerEncoder(
            nn.TransformerEncoderLayer(d_model=d_model, nhead=nhead),
            num_layers=num_layers
        )
        self.fc = nn.Linear(d_model, output_dim)

    def forward(self, x):
        x = self.embedding(x)
        x = x.unsqueeze(1)  # 添加序列维度
        x = self.transformer_encoder(x)
        x = x.squeeze(1)  # 移除序列维度
        x = self.fc(x)
        return x

# 初始化模型
input_dim = train_features.shape[1]
d_model = 64
nhead = 4
num_layers = 2
output_dim = 1
model = TransformerModel(input_dim, d_model, nhead, num_layers, output_dim)

4. 训练模型

import torch.optim as optim

# 定义损失函数和优化器
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# 训练模型
num_epochs = 100
for epoch in range(num_epochs):
    model.train()
    running_loss = 0.0
    for features, targets in train_loader:
        features = features.float()
        targets = targets.float().unsqueeze(1)

        optimizer.zero_grad()
        outputs = model(features)
        loss = criterion(outputs, targets)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()

    print(f'Epoch {epoch+1}/{num_epochs}, Loss: {running_loss/len(train_loader)}')

5. 模型评估

model.eval()
test_loss = 0.0
with torch.no_grad():
    for features, targets in test_loader:
        features = features.float()
        targets = targets.float().unsqueeze(1)

        outputs = model(features)
        loss = criterion(outputs, targets)
        test_loss += loss.item()

print(f'Test Loss: {test_loss/len(test_loader)}')

6. 预测

# 选择一个样本进行预测
sample_features = test_features[0]
sample_features = torch.tensor(sample_features).float().unsqueeze(0)

model.eval()
with torch.no_grad():
    prediction = model(sample_features)
    prediction = scaler_targets.inverse_transform(prediction.numpy())

print(f'Predicted value: {prediction[0][0]}')