Rashtian, Z., Manesh, M. T., Tahsildoost, M., & Zomorodian, Z. S. (2025). Data-driven real-time visualization of urban heat islands using mean radiant temperature for urban design. Energy and Buildings, 115470-. https://doi.org/10.1016/j.enbuild.2025.115470
Abstract
The Mean Radiant Temperature (Tmrt), is a critical indicator for understanding urban thermal comfort and microclimate conditions, particularly in urban areas experiencing higher temperatures compared to rural surroundings. Tmrt is heavily influenced by urban morphology, including building layout, street design, and green spaces which alter airflow, shading, and heat retention. Evaluating geometry alternatives during the early design stages in urban neighborhoods is challenging due to lengthy simulations and the need for extensive expertise in physical models. Recent studies have employed data-driven methods for quick design comparisons and new urban layout evaluations, successfully predicting Thermal indicators of Urban heat Island phenomenon but often limited by the diversity of urban configurations inputs used in training datasets. To address these limitations, this study proposes a novel framework that uses machine learning models to predict Tmrt as the primary indicator. A comprehensive training dataset of 200 cases was generated in Rhino7 using Grasshopper, Ladybug, and Dragonfly plugins. Sensitivity analysis was conducted to assess the impact of input uncertainties on model predictions, and the model’s performance was validated against unseen configurations. Among six machine learning algorithms tested, the CatBoost Regressor achieved the highest accuracy, predicting Tmrt with an R2 = 0.93, RMSE = 4.30 °C, and MAE = 2.34 °C. Validation using 20 additional cases showed an accuracy of R2 = 0.71, RMSE = 3.34 °C, and MAE = 2.27 °C in predicting Tmrt heat maps for new urban configurations. This framework successfully enables real-time Tmrt heat map analysis in simplified cubic neighborhoods within a 3D environment. Additionally, it enhances the temporal and spatial resolution of thermal patterns predictions, offering rapid and detailed insights into various urban design alternatives.