Machine Learning for Urban Heat Island (UHI) Analysis: Predicting Land Surface Temperature (LST) in Urban Environments

Abstract

This study investigates how urban configuration influences the distribution of heat, known as the Urban Heat Island (UHI) effect, in Shiraz, Iran. Several Machine Learning algorithms are employed to analyze Land Surface Temperature (LST) data across various land cover types, including built-up, soil, and vegetation. The analysis reveals that Deep Neural Networks (DNNs) and Extreme Gradient Boosting (XGB) models excel at predicting LST, outperforming other methods. These results highlight the significant impact of land use on LST patterns within the metropolitan regions. Furthermore, the study assesses the influence of specific configuration metrics within each land cover category. This allows researchers to pinpoint which urban morphology features most significantly affect LST. These insights can inform targeted interventions and management strategies implemented to mitigate heat and improve thermal comfort in specific areas of Shiraz.