Snaiki, Reda and Merabtine, Abdelatif.
2025.
« Recent advances on machine learning techniques for urban heat island applications: A review and new horizons ».
Sustainable Cities and Society, vol. 134.
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Abstract
Urban Heat Islands (UHIs) pose a significant global urban challenge, exacerbating heat stress, increasing energy demand, and negatively impacting public health. This review critically analyzes the application of machine learning (ML) strategies for UHI mitigation through an integrated lens encompassing sensing, prediction, optimization, control, and adaptive management. This review starts with a comprehensive evaluation of various data acquisition techniques, such as remote sensing, mobile surveys, and ground-based sensor networks, along with their respective strengths and limitations. Subsequently, the review explores advanced data processing methodologies leveraging ML algorithms for the analysis and interpretation of complex UHI datasets, enabling accurate forecasting and timely interventions. ML-driven prediction and forecasting techniques for UHI are then presented, underscoring the importance of precise and timely predictions for effective mitigation. Further investigation delves into the optimization of UHI mitigation strategies, examining how ML can enhance the effectiveness of approaches such as green infrastructure, cool materials, urban water bodies, and urban planning and design. Finally, the integration of ML insights into flexible adaptation strategies and urban planning processes is discussed, highlighting the necessity for long-term, climate-responsive urban development. The review concludes by assessing the transformative potential and inherent limitations of ML approaches in this domain, outlining critical challenges and promising future research directions for advancing UHI mitigation within rapidly evolving urban environments and under changing climate conditions.
| Item Type: | Peer reviewed article published in a journal |
|---|---|
| Professor: | Professor Snaiki, Reda Merabtine, Abdelatif |
| Affiliation: | Génie de la construction, Génie de la construction |
| Date Deposited: | 21 Nov 2025 20:57 |
| Last Modified: | 10 Jan 2026 15:50 |
| URI: | https://espace2.etsmtl.ca/id/eprint/33037 |
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