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

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

doi:10.1038/s41598-026-44880-z

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

Journal article

Open access

Peer reviewed

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

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

Scientific reports, Vol.16(1), 10594

2026

DOI: https://doi.org/10.1038/s41598-026-44880-z

PMID: 41905990

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Abstract

Climate change

Heatwaves

Extreme heat

Heat vulnerability index

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 ( R² =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.

Details

Title: Multi-scalar risk drivers for a heat vulnerability assessment framework using machine learning algorithms
Authors/Creators: Zecheng Li - University of Malaya
Chng Saun Fong - University of Malaya
Nasrin Aghamohammadi - Murdoch University
Nik Meriam Sulaiman - University of Malaya
Siti Hafizah Ab Hamid - University of Malaya
Publication Details: Scientific reports, Vol.16(1), 10594
Publisher: Nature Publishing Group
Number of pages: 16
Identifiers: 991005875444807891
Murdoch Affiliation: School of Engineering and Energy
Language: English
Resource Type: Journal article

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