📝 SummarXiv

Abstract

Thermal conditions in the urban canopy exhibit stochastic variability driven by varied radiative fluxes and turbulent wind fields, requiring probabilistic rather than deterministic prediction methods. This study presents a probabilistic framework for predicting the spatial and temporal intensity and variability of outdoor thermal comfort in tropical urban environments. The framework integrates ground-measured meteorological data and remote sensing urban morphological data to calculate Physiological Equivalent Temperature (PET), and applies K-means, XGBoost, and Monte Carlo simulations on PET training and inference. The prediction model achieved strong performance, with R2, RMSE, and SMAPE values of 0.93, 0.81 degC, and 1.34% for PET_mean, and 0.85, 0.38 degC, and 10.44% for PET_std, respectively. A case study showed clear spatial heterogeneity of outdoor thermal comfort. Locations with dense tree canopies and vegetated surfaces displayed a normalized percentage of acceptable thermal comfort (NATC) up to 65%, whereas built-up zones dominated by impervious surfaces, such as industrial estates and high-density residential areas, recorded NATC below 30%. Greenery was found to mitigate both the intensity of heat stress and its variability, producing a stable and comfortable microclimate. Daytime PET_std ranged from 4.0-4.5 degC in built-up areas to 1.5-2.0 degC in greenery-covered zones, while nighttime PET_std decreased to 2.2-2.4 degC and 1.2-1.4 degC, respectively. These findings emphasize the critical role of greenery in mitigating thermal variability and enhancing outdoor thermal comfort, while revealing the stochastic nature of thermal comfort across different urban morphologies.