对比7种模型，XGBoost在重庆气温湿度预测中表现最佳。
A Comparative Study of Machine Learning Models for Hourly Forecasting of Air Temperature and Relative Humidity
- 用统一流程预处理数据，构建滞后特征与滚动统计量
- XGBoost预测误差最低，温度MAE仅0.302°C，湿度1.271%
- 适合需要高精度气象预报的山地城市智能管理
准确的短期空气温度和相对湿度预测对城市治理至关重要，尤其在重庆等地形复杂的地区。本研究基于真实开放数据，对比了七种机器学习模型：极端梯度提升（XGBoost）、随机森林、支持向量回归（SVR）、多层感知机（MLP）、决策树、长短期记忆网络（LSTM）以及卷积神经网络-长短期记忆（CNN-LSTM）。所有模型均在统一的数据预处理、滞后特征构建、滚动统计特征和时间序列验证框架下进行系统评估。结果表明，XGBoost整体表现最优，空气温度测试平均绝对误差（MAE）为0.302 °C，相对湿度为1.271%，两项任务平均R²达0.989。研究证实树基集成学习在结构化气象时间序列预测中的高效性，为山地城市的智能化气象预报提供了实践指导。
原文摘要 · Abstract (English)
Accurate short-term forecasting of air temperature and relative humidity is critical for urban management, especially in topographically complex cities such as Chongqing, China. This study compares seven machine learning models: eXtreme Gradient Boosting (XGBoost), Random Forest, Support Vector Regression (SVR), Multi-Layer Perceptron (MLP), Decision Tree, Long Short-Term Memory (LSTM) networks, and Convolutional Neural Network (CNN)-LSTM (CNN-LSTM), for hourly prediction using real-world open data. Based on a unified framework of data preprocessing, lag-feature construction, rolling statistical features, and time-series validation, the models are systematically evaluated in terms of predictive accuracy and robustness. The results show that XGBoost achieves the best overall performance, with a test mean absolute error (MAE) of 0.302 °C for air temperature and 1.271% for relative humidity, together with an average R2 of 0.989 across the two forecasting tasks. These findings demonstrate the strong effectiveness of tree-based ensemble learning for structured meteorological time-series forecasting and provide practical guidance for intelligent meteorological forecasting in mountainous cities.
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