Machine Learning and Spatial Statistical Modeling of Urban Heat Islands under Climate Change Scenarios

Urban heat islands intensify thermal exposure in cities by concentrating heat in dense built environments, reducing nocturnal cooling, and amplifying the effects of regional climate warming. This study compares machine learning and spatial statistical approaches for predicting current urban heat island intensity and projecting mid-century changes under climate change scenarios. The objective is to evaluate whether predictive accuracy, spatial inference, and climate adaptation relevance can be improved through a combined modeling strategy. The analysis uses a 30-m resolution empirical urban climate dataset for a 100 km² metropolitan study area. Land surface temperature, vegetation, impervious surface fraction, building density, albedo, road density, population density, and socioeconomic indicators were assembled for the summer 2023 baseline. Statistically downscaled CMIP6-based projections were incorporated for 2050 under SSP2-4.5 and SSP5-8.5. Four models were implemented and compared: Random Forest, XGBoost, Spatial Error Model, and Geographically Weighted Regression. Model performance was assessed using spatial k-fold cross-validation, while residual spatial dependence was examined using Moran’s I diagnostics. Variable importance, spatial coefficient patterns, and uncertainty intervals were used to compare prediction-oriented and inference-oriented outputs. The results indicate that XGBoost achieved the strongest predictive performance, followed by Random Forest, Geographically Weighted Regression, and the Spatial Error Model. Machine learning models captured nonlinear interactions among vegetation, building density, impervious surface, and albedo, whereas spatial statistical models more effectively reduced residual spatial autocorrelation. Mid-century projections indicated an increase in mean urban heat island intensity of 1.2°C under SSP2-4.5 and 2.8°C under SSP5-8.5. The study is limited by its single metropolitan study area, simplified climate downscaling, and static treatment of urban morphology in future projections. Nevertheless, it provides a transparent empirical framework for comparing predictive machine learning with spatially explicit statistical inference. Its originality lies in systematically linking model comparison, residual spatial diagnostics, and scenario-based urban heat projections for adaptation-oriented urban climate analysis.