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

Ninitha Arumugakani; Madhumathi Anbu; Hourakhsh Ahmadnia; Sudhakarapandian Ranjitharamasamy

doi:10.31127/tuje.1968300

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

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.

Keywords

Supporting Institution

Nil

Project Number

ICCAUA2026EN0410

Ethical Statement

This study does not involve human participants, animal subjects, or any sensitive personal data. The authors declare that this research was conducted in accordance with ethical standards and principles of academic integrity. There are no conflicts of interest among the authors.

References

Al-Yasiri, Q., & Szabó, M. (2023). Numerical analysis of thin building envelope-integrated phase change material towards energy-efficient buildings in severe hot locations. Sustainable Cities and Society, 89. https://doi.org/10.1016/j.scs.2022.104365
Arlot, S., & Celisse, A. (2010). A survey of cross-validation procedures for model selection. Statistics Surveys, 4, 40–79. https://doi.org/10.1214/09-SS054
Asadollah, S., Khan, N., Sharafati, A., Shahid, S., Chung, E., & Wang, X.-J. (2021). Prediction of heat waves using meteorological variables in diverse regions of Iran with advanced machine learning models. Stochastic Environmental Research and Risk Assessment, 36, 1959–1974. https://doi.org/10.1007/s00477-021-02103-z
Badiane, M., & Cunningham, P. (2022). An empirical evaluation of kernels for time series. Artificial Intelligence Review, 55(3), 1803–1820. https://doi.org/10.1007/s10462-021-10050-y
Bonino, G., Galimberti, G., Masina, S., McAdam, R., & Clementi, E. (2024). Machine learning methods to predict sea surface temperature and marine heatwave occurrence: a case study of the Mediterranean Sea. Ocean Science. https://doi.org/10.5194/os-20-417-2024
Breiman, L. (2001). Random Forests (Vol. 45). https://doi.org/10.1023/A:1010933404324
Bröde, P., Fiala, D., Błażejczyk, K., Holmér, I., Jendritzky, G., Kampmann, B., … Havenith, G. (2012). Deriving the operational procedure for the Universal Thermal Climate Index (UTCI). International Journal of Biometeorology, 56(3), 481–494. https://doi.org/10.1007/s00484-011-0454-1
Chai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)? -Arguments against avoiding RMSE in the literature. Geoscientific Model Development, 7(3), 1247–1250. https://doi.org/10.5194/gmd-7-1247-2014

Friedman, J. H. (2001). 999 REITZ LECTURE GREEDY FUNCTION APPROXIMATION: A GRADIENT BOOSTING MACHINE 1. In The Annals of Statistics (Vol. 29). https://doi.org/10.1214/aos/1013203451
Gaur, A., & Deb, C. (2026, July 1). Machine learning methods and approaches for Urban Heat Island (UHI) assessment: A comprehensive review. Renewable and Sustainable Energy Reviews, Vol. 234. Elsevier Ltd. https://doi.org/10.1016/j.rser.2026.116903
Han, B., Luo, Z., Liu, Y., Zhang, T., & Yang, L. (2022). Using Local Climate Zones to investigate Spatio-temporal evolution of thermal environment at the urban regional level: A case study in Xi’an, China. SUSTAINABLE CITIES AND SOCIETY, 76. https://doi.org/10.1016/j.scs.2021.103495
Indraganti, M., & Jawarneh, R. N. (n.d.). Approaches to Measure the Impact of Land Use and Land Cover (LULC) on 2 Land Surface Temperatures (LST) and Outdoor Thermal Comfort: A Systematic Review 4 Shikha. Patel, Shikha and Indraganti, Madhavi and jawarneh, Rana, Approaches to Measure the Impact of Land Use and Land Cover (LULC) on Land Surface Temperatures (LST) and Outdoor Thermal Comfort: A Systematic Review. http://dx.doi.org/10.2139/ssrn.4565518
Iqbal, G., Rosenberger, J., Rosenberger, M., Alam, M. S., Ha, L., Anoruo, E., … Mazzone, T. (2024). A supervised learning tool for heatwave predictions using daily high summer temperatures. Expert Systems, 41. https://doi.org/10.1111/exsy.13656
Kim, Y., & Kim, Y. (2022). Explainable heat-related mortality with random forest and Shapley Additive exPlanations (SHAP) models. Sustainable Cities and Society. https://doi.org/10.1016/j.scs.2022.103677
Mandal, N., Gajria, A., Rangwala, H., & Sarode, T. (2025). Machine Learning-Based Heatwave Modelling for Akola, Maharashtra. 2025 3rd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT), 1292–1297. https://doi.org/10.1109/idciot64235.2025.10915002
Marey, A., Wang, L., Goubran, S., Gaur, A., Lu, H., Leroyer, S., & Belair, S. (2024). Forecasting Urban Land Use Dynamics Through Patch-Generating Land Use Simulation and Markov Chain Integration: A Multi-Scenario Predictive Framework. Sustainability (Switzerland), 16(23). https://doi.org/10.3390/su162310255
Materia, S., García, L. P., Van Straaten, C., O, S., Mamalakis, A., Cavicchia, L., … Donat, M. (2024). Artificial intelligence for climate prediction of extremes: State of the art, challenges, and future perspectives. Wiley Interdisciplinary Reviews: Climate Change, 15. https://doi.org/10.1002/wcc.914
Miloshevich, G., Cozian, B., Abry, P., Borgnat, P., & Bouchet, F. (2022). Probabilistic forecasts of extreme heatwaves using convolutional neural networks in a regime of lack of data. ArXiv, abs/2208.00971. https://doi.org/10.48550/arxiv.2208.00971
Molnar, Christoph. (2022). Interpretable machine learning : a guide for making black box models explainable. Christoph Molnar. https://leanpub.com/interpretable-machine-learning
Moola, P. R., Amma, S., & Alam, H. (2025). A Deep Learning Framework for Spatiotemporal Heatwave Prediction in Oman. 2025 IEEE 6th International Conference on Robotics and Manufacturing Automation (ROMA), 316–321. https://doi.org/10.1109/roma66616.2025.11155723
Narsimha, A., & Sudarshan, V. (2018). Drinking water pollution with respective of fluoride in the semi-arid region of Basara, Nirmal district, Telangana State, India. Data in Brief, 16, 752–757. https://doi.org/10.1016/j.dib.2017.11.087
Oke, C., Walsh, B., Assarkhaniki, Z., Jance, B., Deacon, A., & Lundberg, K. (2022). The City Research and Innovation Agenda: Prioritizing Knowledge Gaps and Policy Processes to Accelerate City Climate Action. Journal of City Climate Policy and Economy, 1(1), 94–110. https://doi.org/10.3138/jccpe-2022.1.1.0007
Qureshi, M. M. U., Ahmed, A. B., Dulmini, A., Khan, M. M. H., & Rois, R. (2025). Developing a seasonal-adjusted machine-learning-based hybrid time series model to forecast heatwave warnings. Scientific Reports, 15. https://doi.org/10.1038/s41598-025-93227-7
Santamouris, M. (2020). Recent progress on urban overheating and heat island research. Integrated assessment of the energy, environmental, vulnerability and health impact. Synergies with the global climate change. Energy and Buildings, 207, 109482. https://doi.org/https://doi.org/10.1016/j.enbuild.2019.109482
Santhoshkumar, B., Sathyamoorthy, N. K., Geethalakshmi, V., Dheebakaran, G., Boomiraj, K., Manikandan, N., & Selva Kumar, M. (n.d.). Prediction of drought-risk zones in Tamil Nadu using historical and global climate model data. https://doi.org/10.18520/cs/v127/i3/340-351
Schlegel, R., Oliver, E., Hobday, A., & Smit, A. (2019). Detecting Marine Heatwaves With Sub-Optimal Data. Frontiers in Marine Science. https://doi.org/10.3389/fmars.2019.00737
Shi, Z., Yang, J., Zhang, Y., Xiao, X., & Xia, J. C. (2022). Urban ventilation corridors and spatiotemporal divergence patterns of urban heat island intensity: a local climate zone perspective. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 29(49), 74394–74406. https://doi.org/10.1007/s11356-022-21037-9
Sultana, S. S., Mumu, S. S., Fahim, R. R., Neha, N. N., & Mahbub, Md. S. (2024). Regional Heatwave Prediction Using Deep Learning Models In Bangladesh. 2024 2nd International Conference on Information and Communication Technology (ICICT), 125–129. https://doi.org/10.1109/icict64387.2024.10839717
Suthar, G., Kaul, N., Khandelwal, S., & Singh, S. (2024). Predicting land surface temperature and examining its relationship with air pollution and urban parameters in Bengaluru: A machine learning approach. Urban Climate. https://doi.org/10.1016/j.uclim.2024.101830
Tanoori, G., Soltani, A., & Modiri, A. (2024). Machine Learning for Urban Heat Island (UHI) Analysis: Predicting Land Surface Temperature (LST) in Urban Environments. Urban Climate. https://doi.org/10.1016/j.uclim.2024.101962
Wang, H., Yang, J., Chen, G., Ren, C., & Zhang, J. (2023). Machine learning applications on air temperature prediction in the urban canopy layer: A critical review of 2011–2022. Urban Climate. https://doi.org/10.1016/j.uclim.2023.101499
Xi, T., Wang, M., Cao, E., Li, J., Wang, Y., & Sa’ad, S. U. (2024). Preliminary Research on Outdoor Thermal Comfort Evaluation in Severe Cold Regions by Machine Learning. Buildings. https://doi.org/10.3390/buildings14010284
Xiong, S., Chen, X., Zhang, H., & Wang, M. (2024). Domain Adaptation-Based Deep Learning Framework for Android Malware Detection Across Diverse Distributions. Artificial Intelligence Advances, 6(1), 13–24. https://doi.org/10.30564/aia.v6i1.6718
Yaseen, M., Latif, Y., Waseem, M., Leta, M. K., Abbas, S., & Bhatti, H. A. (2022). Contemporary Trends in High and Low River Flows in Upper Indus Basin, Pakistan. Water (Switzerland), 14(3). https://doi.org/10.3390/w14030337
Zhang, J., Yu, J., Jia, C., Dai, Y., Zhu, Y., Huang, Y., & Wu, L. (2022). Tomographic Inversion of the Ionosphere by Rejecting Abnormal Corrections and Rays. Atmosphere, 13(12). https://doi.org/10.3390/atmos13121954
Durap, A. (2025). Machine learning-based wind speed prediction using random forest: a cross-validated analysis for renewable energy applications. Turkish Journal of Engineering, 9(3), 508–518. https://doi.org/10.31127/tuje.1624354
Comert, M., & Yıldız, A. (2022). A novel artificial neural network model for forecasting electricity demand enhanced with population-weighted temperature mean and the unemployment rate. Turkish Journal of Engineering, 6(2), 178–189. https://doi.org/10.31127/tuje.903876
Mogaraju, J. K. (2024). Machine learning empowered the prediction of geolocation using groundwater quality variables over YSR district of India. Turkish Journal of Engineering, 8(1), 31–45. https://doi.org/10.31127/tuje.1223779
Aliyazıcıoğlu, K., Beker, F., Topaloğlu, R. H., Bilgilioğlu, B. B., & Çömert, R. (2021). Temporal monitoring of land use/land cover change in Kahramanmaraş city. Turkish Journal of Engineering, 5(3), 134–140. https://doi.org/10.31127/tuje.707156
Patil, R., & Surawar, M. (2023). Impact of Urban Heat Island on Formation of Precipitation in Indian Western Coastal Cities. Journal of Contemporary Urban Affairs, 7(2), 38–55. https://doi.org/10.25034/ijcua.2023.v7n2-3
Sadat, N., Sheikh, H., & Asaduzzaman, M. (2024). Evaluating Urban Heat Island Mitigation Strategies in Rajshahi, Using ENVI-Met: A Remote Sensing Approach. Journal of Contemporary Urban Affairs, 8(2). https://doi.org/10.25034/ijcua.2024.v8n2-15

Details

Primary Language

English

Subjects

Architectural Engineering

Journal Section

Research Article

Authors

Ninitha Arumugakani
0009-0000-3934-1070
India

Madhumathi Anbu ^*
0000-0002-7478-1183
India

Hourakhsh Ahmadnia
0000-0002-1083-280X
Türkiye

Sudhakarapandian Ranjitharamasamy This is me
0000-0003-4925-0054
India

Publication Date

October 16, 2026

Submission Date

June 10, 2026

Acceptance Date

July 27, 2026

Published in Issue

Year 2026 Volume: 10 Number: 4

DOI

https://doi.org/10.31127/tuje.1968300

IZ

https://izlik.org/JA92JP85BW

Cite

RIS / Bibtex

APA

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, 10(4), 1504-1518. https://doi.org/10.31127/tuje.1968300

AMA

1.Arumugakani N, Anbu M, Ahmadnia H, Ranjitharamasamy S. Predictive Modeling of Urban Heat Stress Using Machine Learning: A 2050 UTCI-Based Assessment. TUJE. 2026;10(4):1504-1518. doi:10.31127/tuje.1968300

Chicago

Arumugakani, Ninitha, Madhumathi Anbu, Hourakhsh Ahmadnia, and Sudhakarapandian Ranjitharamasamy. 2026. “Predictive Modeling of Urban Heat Stress Using Machine Learning: A 2050 UTCI-Based Assessment”. Turkish Journal of Engineering 10 (4): 1504-18. https://doi.org/10.31127/tuje.1968300.

EndNote

Arumugakani N, Anbu M, Ahmadnia H, Ranjitharamasamy S (October 1, 2026) Predictive Modeling of Urban Heat Stress Using Machine Learning: A 2050 UTCI-Based Assessment. Turkish Journal of Engineering 10 4 1504–1518.

IEEE

[1]N. Arumugakani, M. Anbu, H. Ahmadnia, and S. Ranjitharamasamy, “Predictive Modeling of Urban Heat Stress Using Machine Learning: A 2050 UTCI-Based Assessment”, TUJE, vol. 10, no. 4, pp. 1504–1518, Oct. 2026, doi: 10.31127/tuje.1968300.

ISNAD

Arumugakani, Ninitha - Anbu, Madhumathi - Ahmadnia, Hourakhsh - Ranjitharamasamy, Sudhakarapandian. “Predictive Modeling of Urban Heat Stress Using Machine Learning: A 2050 UTCI-Based Assessment”. Turkish Journal of Engineering 10/4 (October 1, 2026): 1504-1518. https://doi.org/10.31127/tuje.1968300.

JAMA

1.Arumugakani N, Anbu M, Ahmadnia H, Ranjitharamasamy S. Predictive Modeling of Urban Heat Stress Using Machine Learning: A 2050 UTCI-Based Assessment. TUJE. 2026;10:1504–1518.

MLA

Arumugakani, Ninitha, et al. “Predictive Modeling of Urban Heat Stress Using Machine Learning: A 2050 UTCI-Based Assessment”. Turkish Journal of Engineering, vol. 10, no. 4, Oct. 2026, pp. 1504-18, doi:10.31127/tuje.1968300.

Vancouver

1.Ninitha Arumugakani, Madhumathi Anbu, Hourakhsh Ahmadnia, Sudhakarapandian Ranjitharamasamy. Predictive Modeling of Urban Heat Stress Using Machine Learning: A 2050 UTCI-Based Assessment. TUJE. 2026 Oct. 1;10(4):1504-18. doi:10.31127/tuje.1968300