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Dergiler / Turkish Journal of Engineering / 2026 / Volume: 10 Issue: 4

Predictive Modeling of Urban Heat Stress Using Machine Learning: A 2050 UTCI-Based Assessment

Ninitha Arumugakani(Vellore Institute of Technology)0009-0000-3934-1070
Madhumathi Anbu(Vellore Institute of Technology)0000-0002-7478-1183
Hourakhsh Ahmadnia(ALANYA HAMDULLAH EMIN PASA UNIVERSITY)0000-0002-1083-280X
Sudhakarapandian Ranjitharamasamy(Vellore Institute of Technology)0000-0003-4925-0054
Sayfa
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Yayın Tarihi
2026-10-16
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The results strengthen the acceptance of data-driven ML frameworks for the application in urban planning and climate-responsive architectural design practice.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Urban heat islands and increasingly severe thermal stress pose critical challenges to sustainable urban development, yet physics-based simulation tools remain computationally expensive and ill-suited to early-stage climate-responsive design.
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Temel Bulgular & Veri:This study applies machine learning to predict outdoor air and surface temperature using 20 years (2005-2025) of daily maximum temperature data ranging from 15°C to 40°C.
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Sonuç & Katkı:The results strengthen the acceptance of data-driven ML frameworks for the application in urban planning and climate-responsive architectural design practice.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:2005-2025 Verileri
⚙️Değişkenler:Machine Learning · Temperature Projection · Urban microclimate

Abstract

Urban heat islands and increasingly severe thermal stress pose critical challenges to sustainable urban development, yet physics-based simulation tools remain computationally expensive and ill-suited to early-stage climate-responsive design. This study applies machine learning to predict outdoor air and surface temperature using 20 years (2005-2025) of daily maximum temperature data ranging from 15°C to 40°C. Four algorithms were evaluated: Linear Regression, Random Forest, Gradient Boosting and Extreme Gradient Boosting (XG Boost). Gradient Boosting achieved the best performance with RMSE = 1.869, MAE = 1.405, and R² = 0.708, indicating robust predictive ability across diurnal temperature and seasonal patterns. 2050 projections, based on direct extrapolation of the trained model's seasonal temperature pattern, show a shift toward moderate-to-strong Universal Thermal Climate Index (UTCI) heat stress conditions across the majority of the year, underscoring the need for climate-responsive urban design even under conservative, non-scenario-scaled projections. The results strengthen the acceptance of data-driven ML frameworks for the application in urban planning and climate-responsive architectural design practice.

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Bu Makaleye Atıf Yap

Arumugakani, N., Anbu, M., Ahmadnia, H., Ranjitharamasamy, S. (2026). Predictive Modeling of Urban Heat Stress Using Machine Learning: A 2050 UTCI-Based Assessment. Turkish Journal of Engineering, Volume: 10 Issue: 4. https://doi.org/10.31127/tuje.1968300

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