A Comparative Study of Machine Learning Models for Hourly Forecasting of Air Temperature and Relative Humidity
It addresses the problem of accurate short-term weather forecasting for urban management in topographically complex cities, but is incremental as it applies existing methods to a specific dataset.
This study compared seven machine learning models for hourly forecasting of air temperature and relative humidity in Chongqing, China, finding that XGBoost performed best with a test MAE of 0.302°C for temperature and 1.271% for humidity, and an average R² of 0.989.
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.