用python 编写一个能计算预测地下水位的小程序

在Python中编写一个简单的地下水位预测小程序，我们可以利用机器学习中的线性回归模型作为基础示例。这里我们假设你有一个历史数据集，其中包含了影响地下水位的因素（如降雨量、气温等）和对应的地下水位观测值。

# 导入所需库
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error

# 假设data.csv是你的数据文件
def load_data(file_path):
    data = pd.read_csv(file_path)
    return data

def preprocess_data(data):
    # 数据预处理步骤，如缺失值填充、特征工程等
    X = data[['rainfall', 'temperature']]  # 假设这两个是影响因素
    y = data['groundwater_level']
    return X, y

def split_data(X, y):
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
    return X_train, X_test, y_train, y_test

def train_model(X_train, y_train):
    model = LinearRegression()
    model.fit(X_train, y_train)
    return model

def predict(model, X_test):
    groundwater_levels_pred = model.predict(X_test)
    return groundwater_levels_pred

def main():
    data = load_data('data.csv')
    X, y = preprocess_data(data)
    X_train, X_test, y_train, y_test = split_data(X, y)
    model = train_model(X_train, y_train)
    predictions = predict(model, X_test)
    print("预测的地下水位：", predictions)

if __name__ == "__main__":
    main()