Abstract
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.
Abstract (translated)
空气温度与相对湿度的精准短期预报对城市管理至关重要，尤其在地形复杂的城市（如中国重庆）更是如此。本研究采用真实开放数据，对比分析了七种机器学习模型（极限梯度提升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及卷积神经网络-长短期记忆网络CNN-LSTM）的小时级预测效果。基于统一的数据预处理、滞后特征构建、滚动统计特征及时序验证框架，研究从预测精度与稳健性两个维度对模型进行了系统评估。结果表明：XGBoost综合表现最优，在气温预测中测试集平均绝对误差（MAE）达0.302℃，相对湿度预测MAE为1.271%，两项任务平均R²值高达0.989。这些发现验证了基于树的集成学习方法对结构化气象时间序列预测的显著有效性，并为山地城市智能气象预报提供了实践指导。
URL
https://arxiv.org/abs/2603.23282
