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<p>Rapidly urbanising South Asian cities face increasing thermal risk, but comparative, physically grounded studies of their structural capacity to withstand heat loading are rare. We utilize a stacked ensemble of five machine learning architectures (Random Forest, Gradient Boosting, XGBoost, LightGBM, and a deep learning model) to forecast daytime land surface temperature (LST) in 15 cities with dry, tropical, montane, and coastal climates. The ensemble achieved R² = 0.971 and RMSE ≈ 1.18&#xa0;°C. We introduce the Urban Heat Resilience Index (UHRI), a weighted composite of thermal stress, cooling capacity, environmental buffering, and adaptive capacity sub-components, and use it to assess structural resilience discrepancies and estimate their trajectory to 2075. UHRI scores vary nearly twofold, from 35.2 (Delhi) to 67.1 (Thimphu). Critically, high-resilience cities show no significant warming trends, showing that structural resilience provides measurable resistance to LST acceleration. These findings offer a regionally validated, paradigm for prioritising urban heat adaption investment in one of the world’s most thermally exposed urbanizing regions.</p>

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Eroding heat resilience in South Asian cities under observed warming trends

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月26日

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  • Bhawna Yadav,
  • Laxmi Kant Sharma,
  • Kariya Ishita Bhaveshkumar¹

摘要

Rapidly urbanising South Asian cities face increasing thermal risk, but comparative, physically grounded studies of their structural capacity to withstand heat loading are rare. We utilize a stacked ensemble of five machine learning architectures (Random Forest, Gradient Boosting, XGBoost, LightGBM, and a deep learning model) to forecast daytime land surface temperature (LST) in 15 cities with dry, tropical, montane, and coastal climates. The ensemble achieved R² = 0.971 and RMSE ≈ 1.18 °C. We introduce the Urban Heat Resilience Index (UHRI), a weighted composite of thermal stress, cooling capacity, environmental buffering, and adaptive capacity sub-components, and use it to assess structural resilience discrepancies and estimate their trajectory to 2075. UHRI scores vary nearly twofold, from 35.2 (Delhi) to 67.1 (Thimphu). Critically, high-resilience cities show no significant warming trends, showing that structural resilience provides measurable resistance to LST acceleration. These findings offer a regionally validated, paradigm for prioritising urban heat adaption investment in one of the world’s most thermally exposed urbanizing regions.