UHIQ: An Interactive Explainable Framework for Urban Heat Island Prediction Using Physics-Informed Regression and XGBoost
Abstract
Abstract Urban heat islands (UHIs) are an increasingly significant consequence of urbanization and climate change, contributing to elevated temperatures that affect public health, energy consumption, and urban sustainability. Accurate and interpretable prediction of UHI intensity is essential for supporting effective heat mitigation strategies and informed urban planning. This study presents UHIQ, an interactive urban heat island prediction framework that combines a physics-informed linear regression model with an XGBoost machine learning model to estimate annual daytime UHI intensity using vegetation and elevation-related environmental variables. To improve model transparency, the framework incorporates permutation feature importance and sensitivity analysis, enabling users to examine how changes in environmental conditions influence predicted UHI intensity. An interactive dashboard further supports scenario-based exploration by allowing users to compare predictions from both modeling approaches in real time. The machine learning model achieved higher predictive performance than the physics-informed model, while both models consistently identified vegetation as a key factor influencing urban heat intensity. By integrating predictive modeling, explainability, and interactive visualization within a unified framework, UHIQ provides an accessible decision-support tool that can assist researchers, planners, and policymakers in understanding urban heat dynamics and evaluating…
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