Sustainable Urban Heat Island Mitigation Through Machine Learning: Integrating Physical and Social Determinants for Evidence-Based Urban Policy

Amatul Quadeer Syeda, Krystel K. Castillo-Villar () and Adel Alaeddini
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Amatul Quadeer Syeda: Department of Mechanical, Aerospace, and Industrial Engineering, Texas Sustainable Energy Research Institute, The University of Texas, San Antonio, TX 78249, USA
Krystel K. Castillo-Villar: Department of Mechanical, Aerospace, and Industrial Engineering, Texas Sustainable Energy Research Institute, The University of Texas, San Antonio, TX 78249, USA
Adel Alaeddini: Mechanical Engineering Department, Southern Methodist University, 6425 Boaz Lane, Dallas, TX 75205, USA

Sustainability, 2025, vol. 17, issue 15, 1-25

Abstract: Urban heat islands (UHIs) are a growing sustainability challenge impacting public health, energy use, and climate resilience, especially in hot, arid cities like San Antonio, Texas, where land surface temperatures reach up to 47.63 °C. This study advances a data-driven, interdisciplinary approach to UHI mitigation by integrating Machine Learning (ML) with physical and socio-demographic data for sustainable urban planning. Using high-resolution spatial data across five functional zones (residential, commercial, industrial, official, and downtown), we apply three ML models, Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting Machine (GBM), to predict land surface temperature (LST). The models incorporate both environmental variables, such as imperviousness, Normalized Difference Vegetation Index (NDVI), building area, and solar influx, and social determinants, such as population density, income, education, and age distribution. SVM achieved the highest R 2 (0.870), while RF yielded the lowest RMSE (0.488 °C), confirming robust predictive performance. Key predictors of elevated LST included imperviousness, building area, solar influx, and NDVI. Our results underscore the need for zone-specific strategies like more greenery, less impervious cover, and improved building design. These findings offer actionable insights for urban planners and policymakers seeking to develop equitable and sustainable UHI mitigation strategies aligned with climate adaptation and environmental justice goals.

Keywords: Urban Heat Island (UHI); urban planning; machine learning; extreme weather; thermal risk mapping; climate resilience (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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