Intra-Urban Induced Heating Assessment in Kuwait’s Desert Metropolis Using Explainable Machine Learning

Abstract

Intra-urban induced heating (IUIH) in hot desert cities exhibits distinct patterns and complex diurnal interactions with built environment features, differing significantly from those in temperate areas and remains not fully understood. Understanding how various built environment features contribute to intra-urban thermal variability is an essential first step in developing sub-diurnal targeted heat mitigation strategies. This study presents a data-driven examination of IUIH dynamics in Kuwait’s desert metropolis. Near-surface air temperature observations were collected using high-resolution loop-type traverses at selected hours during a representative summer day to determine IUIH variability. The diurnal impacts of built environment features were modeled using an ensemble learning approach and interpreted with SHapley Additive exPlanations. Among several candidate machine learning regressors evaluated, Random Forest demonstrated strong predictive power (R2 = 0.954) with acceptable error (RMSE = 0.096, MAPE = 0.001) and least bias (MBE = 0.008). The study’s significance lies in its assessment framework that emphasizes explainability of sub-diurnal dynamics, offering detailed insights that challenge traditional assumptions and inform both immediate local climate interventions and strategic urban planning. The findings reveal that simple day-evening comparatives might overlook nuanced sub-diurnal dynamics, such as potential irrigation-induced warming by shrubs observed at mid-day and the complex trade-offs between radiative and transpirative processes by trees in the afternoon and evening. Additionally, the study identifies cooling effects associated with natural land cover, presenting a critical optimization challenge between compact and open urban forms to effectively modulate near-surface air temperatures.