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<p>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, <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(PM_{2.5}\)</EquationSource> </InlineEquation>) yielded superior all-caused mortality prediction. The findings reveal that the framework incorporating ambient atmospheric conditions achieved superior predictive power (<InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(R^2\)</EquationSource> </InlineEquation>=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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月30日

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  • Zecheng Li,
  • Chng Saun Fong,
  • Nasrin Aghamohammadi,
  • Nik Meriam Sulaiman,
  • Siti Hafizah Ab Hamid

摘要

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, \(PM_{2.5}\) ) yielded superior all-caused mortality prediction. The findings reveal that the framework incorporating ambient atmospheric conditions achieved superior predictive power ( \(R^2\) =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.