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<p>Mediterranean cities are increasingly vulnerable to extreme heat events, driven by rapid urbanization and climate change. This study proposes a high-resolution framework for assessing heat-health risk (HHR) in Algiers, Algeria, by integrating the Heat Health Risk Index (HHRI) and Surface Urban Heat Island (SUHI) metrics within the Local Climate Zone (LCZ) classification 2020 system. Drawing on multi-temporal satellite data (2015–2023), demographic information, and meteorological records, we generated hazard, exposure, and vulnerability layers, with variable weighting derived from Principal Component Analysis (PCA). SUHI was estimated using Landsat-based Land Surface Temperature (LST) data, referencing rural LCZs as thermal baselines. Unsupervised K-means clustering was employed to classify combined HHRI–SUHI data, revealing four distinct urban heat risk profiles. The results indicate that LCZs 4, 5, and 8 are most affected by compounded heat-health risks, while LCZs 4, 6, and 8 display the highest surface heat accumulation. The resulting typologies identify zones where thermal stress intersects with social vulnerability, offering valuable insights for targeted adaptation. This is the first study in North Africa and the Mediterranean to apply this integrated clustering approach, demonstrating its applicability to other heat-prone, data-scarce urban environments.</p> Graphical Abstract <p>This graphical abstract illustrates an integrated geospatial and machine learning framework developed to assess urban heat-health risk in Algiers, Algeria a densely populated Mediterranean city facing rising exposure to extreme heat events. The study combines multi-year satellite-derived Land Surface Temperature (LST), demographic indicators, and meteorological data from 2015 to 2023. Using the Local Climate Zone (LCZ) classification, the urban landscape was stratified based on morphological and thermal characteristics. The Heat Health Risk Index (HHRI) was computed through a hazard–exposure–vulnerability framework, with variable weighting based on Principal Component Analysis (PCA). In parallel, SUHI intensity was derived by benchmarking urban LST values against rural LCZs. Unsupervised K-means clustering was then used to generate compound spatial typologies, revealing four distinct urban risk profiles. Results show that LCZs 4, 5, and 8 host the highest risk zones, where intense thermal stress overlaps with sensitive populations. The framework provides a replicable, high-resolution approach for mapping heat vulnerability and informs targeted adaptation strategies in Mediterranean and climate-vulnerable cities worldwide.</p>

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Machine Learning-Driven Mapping of Heatwave Health Risks Across Local Climate Zones in a Mediterranean Context

  • 期刊论文

  • 发表日期：

    2025年10月17日

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  • Dyna Chourouk Zitouni,
  • Djihed Berkouk,
  • Mohamed Elhadi Matallah,
  • Mohamed Akram Eddine Ben Ratmia,
  • Ayyoob Sharifi,
  • Shady Attia

摘要

Mediterranean cities are increasingly vulnerable to extreme heat events, driven by rapid urbanization and climate change. This study proposes a high-resolution framework for assessing heat-health risk (HHR) in Algiers, Algeria, by integrating the Heat Health Risk Index (HHRI) and Surface Urban Heat Island (SUHI) metrics within the Local Climate Zone (LCZ) classification 2020 system. Drawing on multi-temporal satellite data (2015–2023), demographic information, and meteorological records, we generated hazard, exposure, and vulnerability layers, with variable weighting derived from Principal Component Analysis (PCA). SUHI was estimated using Landsat-based Land Surface Temperature (LST) data, referencing rural LCZs as thermal baselines. Unsupervised K-means clustering was employed to classify combined HHRI–SUHI data, revealing four distinct urban heat risk profiles. The results indicate that LCZs 4, 5, and 8 are most affected by compounded heat-health risks, while LCZs 4, 6, and 8 display the highest surface heat accumulation. The resulting typologies identify zones where thermal stress intersects with social vulnerability, offering valuable insights for targeted adaptation. This is the first study in North Africa and the Mediterranean to apply this integrated clustering approach, demonstrating its applicability to other heat-prone, data-scarce urban environments.

Graphical Abstract

This graphical abstract illustrates an integrated geospatial and machine learning framework developed to assess urban heat-health risk in Algiers, Algeria a densely populated Mediterranean city facing rising exposure to extreme heat events. The study combines multi-year satellite-derived Land Surface Temperature (LST), demographic indicators, and meteorological data from 2015 to 2023. Using the Local Climate Zone (LCZ) classification, the urban landscape was stratified based on morphological and thermal characteristics. The Heat Health Risk Index (HHRI) was computed through a hazard–exposure–vulnerability framework, with variable weighting based on Principal Component Analysis (PCA). In parallel, SUHI intensity was derived by benchmarking urban LST values against rural LCZs. Unsupervised K-means clustering was then used to generate compound spatial typologies, revealing four distinct urban risk profiles. Results show that LCZs 4, 5, and 8 host the highest risk zones, where intense thermal stress overlaps with sensitive populations. The framework provides a replicable, high-resolution approach for mapping heat vulnerability and informs targeted adaptation strategies in Mediterranean and climate-vulnerable cities worldwide.