[2603.23282] A Comparative Study of Machine Learning Models for Hourly Forecasting of Air Temperature and Relative Humidity

Computer Science > Machine Learning arXiv:2603.23282 (cs) [Submitted on 24 Mar 2026] Title:A Comparative Study of Machine Learning Models for Hourly Forecasting of Air Temperature and Relative Humidity Authors:Jiaqi Dong View a PDF of the paper titled A Comparative Study of Machine Learning Models for Hourly Forecasting of Air Temperature and Relative Humidity, by Jiaqi Dong View PDF 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 fo...