A probabilistic framework for predicting spatiotemporal intensity and variability of outdoor thermal comfort
Authors:
Shisheng Chen, Ruohan Xu, Nyuk Hien Wong, Shanshan Tong, Jiashuo Wang, and Matthaios Santamouris
(1) School of Architecture and Urban-rural Planning, Fuzhou University, China
(2) Department of the Built Environment, National University of Singapore, Singapore
(3) School of Built Environment, University of New South Wales, Australia
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 integrated ground measured meteorological data and remote sensing urban morphological data to calculate Physiological Equivalent Temperature (PET), and uses K-means, XGBoost and Monte Carlo simulations on PET’s training and inference. The prediction model achieved strong predictive performance, with R², RMSE, and SMAPE values of 0.93, 0.81 °C, and 1.34% for , and 0.85, 0.38 °C, and 10.44% for , respectively. Case study showed clear spatial heterogeneity of outdoor thermal comfort. Locations with dense tree canopies and vegetated surfaces displayed the 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 ranged from 4.0 to 4.5 °C in built-up areas to 1.5–2.0 °C in greenery-covered zones, while nighttime decreased to 2.2–2.4 °C and 1.2–1.4 °C, 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.
Keywords:
Outdoor Thermal Comfort, Physiological Equivalent Temperature, Heat Stress, Clustering, Regression, Probabilistic Prediction