基于传统机器学习算法估算FR-Pue站点的潜热通量代码

以下是使用Python实现基于传统机器学习算法估算FR-Pue站点的潜热通量的代码示例：

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error

# 读取数据
df = pd.read_csv('data.csv')

# 特征选择
X = df[['Temperature', 'Humidity', 'BuildingArea', 'Orientation', 'WindowArea']]
y = df['SensibleHeat']

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)

# 线性回归模型
lr_model = LinearRegression()
lr_model.fit(X_train, y_train)
lr_pred = lr_model.predict(X_test)
lr_mse = mean_squared_error(y_test, lr_pred)
print('Linear Regression MSE:', lr_mse)

# 决策树模型
dt_model = DecisionTreeRegressor(random_state=0)
dt_model.fit(X_train, y_train)
dt_pred = dt_model.predict(X_test)
dt_mse = mean_squared_error(y_test, dt_pred)
print('Decision Tree MSE:', dt_mse)

# 随机森林模型
rf_model = RandomForestRegressor(n_estimators=100, random_state=0)
rf_model.fit(X_train, y_train)
rf_pred = rf_model.predict(X_test)
rf_mse = mean_squared_error(y_test, rf_pred)
print('Random Forest MSE:', rf_mse)

其中，`data.csv`是包含FR-Pue站点历史气象数据和建筑物信息的数据文件，特征选择中选取了气温、湿度、建筑物面积、朝向、窗户面积等特征，使用`train_test_split`函数将数据集划分为训练集和测试集，分别使用线性回归、决策树、随机森林三种机器学习算法进行模型训练和预测，并计算均方误差（MSE）作为模型性能的评估指标。