Multi-scalar risk drivers for a heat vulnerability assessment framework using machine learning algorithms.

This study aims to address the challenge of quantifying amplified heat-related health risks in tropical nations by developing and validating a novel, data-driven framework in Malaysia to deconstruct the complex interplay between social vulnerability and environmental exposure. Methodologically, we constructed a Heat Vulnerability Index (HVI) and employed a Random Forest model to systematically evaluate whether integrating HVI with local land surface physical characteristics or with ambient atmospheric conditions (Universal Thermal Climate Index (UTCI), Ozone, [Formula: see text]) yielded superior all-caused mortality prediction. The findings reveal that the framework incorporating ambient atmospheric conditions achieved superior predictive power ([Formula: see text]=0.8623), with the HVI, Ozone, and UTCI identified as the dominant predictors, while SHapley Additive exPlanations analysis further uncovered significant spatial heterogeneity in their impacts on mortality. Ultimately, this research provides a robust, evidence-based tool for policymakers, demonstrating that in a tropical context, combining macro-scale ambient atmospheric conditions with intrinsic social vulnerability is the most effective strategy for identifying high-risk communities and prioritizing targeted interventions, establishing a transferable protocol to mitigate heat-related health risks across the broader tropical zone.