Urban Heat Island Mitigation through AI-Optimized Green Infrastructure Planning

Priya Nair; Carlos Mendez

doi:10.55001/ijeer.v1i2.59

Abstract

Urban heat islands (UHIs) elevate city-center temperatures by 2-8°C relative to surrounding rural areas, increasing cooling demand, mortality, and energy consumption. We develop an AI-driven green infrastructure optimization framework that integrates high-resolution land surface temperature (LST) satellite data, urban morphology parameters, and a graph neural network (GNN) surrogate model trained on 15,000 CFD thermal simulations. Applied to three megacities — Jakarta, Madrid, and Houston — the framework identifies optimal spatial configurations of urban trees, green roofs, and cool pavements that maximize UHI reduction per unit investment. The optimized plans achieve predicted daytime LST reductions of 1.8-3.2°C in hotspot districts, corresponding to 12-22% reductions in peak cooling electricity demand, while maintaining equitable green space distribution across low- and high-income neighborhoods.

Author Biographies

Priya Nair Department of Civil and Environmental Engineering, National University of Singapore, Singapore 117576

Priya Nair is a research fellow at Department of Civil and Environmental Engineering, National University of Singapore, Singapore 117576. Their research focuses on data analytics, with over 79 publications in peer-reviewed journals.
Carlos Mendez Centro de Investigación en Energía y Medio Ambiente, Universidad Politécnica de Madrid, 28040 Madrid, Spain

Carlos Mendez is a senior researcher at Centro de Investigación en Energía y Medio Ambiente, Universidad Politécnica de Madrid, 28040 Madrid, Spain. Their research focuses on biomedical engineering, with over 57 publications in peer-reviewed journals.