Urban morphology impacts on urban microclimate using artificial intelligence – a review
Urban morphology, defined by the characteristics and spatial arrangement of urban structures, significantly affects urban microclimate in terms of thermal environments, wind dynamics, energy use, and outdoor air quality. Despite extensive research in this field, these effects are intensified by clim...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-12-01
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| Series: | City and Environment Interactions |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590252025000352 |
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| author | Ahmed Marey Jiwei Zou Sherif Goubran Liangzhu Leon Wang Abhishek Gaur |
| author_facet | Ahmed Marey Jiwei Zou Sherif Goubran Liangzhu Leon Wang Abhishek Gaur |
| author_sort | Ahmed Marey |
| collection | DOAJ |
| description | Urban morphology, defined by the characteristics and spatial arrangement of urban structures, significantly affects urban microclimate in terms of thermal environments, wind dynamics, energy use, and outdoor air quality. Despite extensive research in this field, these effects are intensified by climate change and rapid urbanization, posing challenges to urban sustainability, such as poor air quality, increased energy demands, and pedestrian discomfort. While artificial intelligence (AI) and machine learning (ML) offer promising solutions for addressing these challenges, the field lacks standardized approaches for implementing these technologies. By leveraging urban morphology indicators such as sky view factor, building density, and green space ratio, AI models can analyze complex interactions across various spatiotemporal scales. However, significant variability in methodologies, indicators, and datasets limits the generalizability and applicability of these techniques. By synthesizing 111 studies over the last decade utilizing urban morphology and AI models to predict urban microclimate, this review aims to bridge these gaps and highlight AI’s unique potential to contribute to the field. Analyzed studies reported that key urban morphology indicators, particularly building density and height, explain up to 75% of land surface temperature variance across seasons, while sky view factor accounts for over 67% of heat exposure variations in urban environments, with these findings emerging from multiple independent investigations across diverse urban contexts. Random Forest emerges as the most widely adopted AI technique, demonstrating robust performance across different applications. Emerging trends, such as hybrid approaches combining AI with physics-based models, are highlighted as promising avenues for advancing the field. Our review identifies the need for standardized frameworks and datasets to enhance model applicability. The study presents actionable insights for climate-responsive urban planning and lays the groundwork for interdisciplinary studies, enabling the development of resilient, sustainable urban environments amid the growing challenges of urbanization and climate change. |
| format | Article |
| id | doaj-art-80760c3f5f5e4ab3ac3a1bb46eada05f |
| institution | Kabale University |
| issn | 2590-2520 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | City and Environment Interactions |
| spelling | doaj-art-80760c3f5f5e4ab3ac3a1bb46eada05f2025-08-20T03:30:19ZengElsevierCity and Environment Interactions2590-25202025-12-012810022110.1016/j.cacint.2025.100221Urban morphology impacts on urban microclimate using artificial intelligence – a reviewAhmed Marey0Jiwei Zou1Sherif Goubran2Liangzhu Leon Wang3Abhishek Gaur4Centre for Zero Energy Building Studies, Department of Building, Civil and Environmental Engineering, Concordia University, Montreal H3G 1M8 Canada; Building and Climate Interface, Construction Research Centre, National Research Council Canada, Ottawa, ON K1A 0R6, CanadaCentre for Zero Energy Building Studies, Department of Building, Civil and Environmental Engineering, Concordia University, Montreal H3G 1M8 Canada; Building and Climate Interface, Construction Research Centre, National Research Council Canada, Ottawa, ON K1A 0R6, CanadaDepartment of Architecture, School of Sciences and Engineering, The American University in Cairo, New Cairo 11835, EgyptCentre for Zero Energy Building Studies, Department of Building, Civil and Environmental Engineering, Concordia University, Montreal H3G 1M8 Canada; Corresponding author at: Centre for Zero Energy Building Studies, Department of Building, Civil and Environmental Engineering, Concordia University, 1455 de Maisonneuve Blvd. West, Montreal, Quebec H3G 1M8, Canada.Building and Climate Interface, Construction Research Centre, National Research Council Canada, Ottawa, ON K1A 0R6, CanadaUrban morphology, defined by the characteristics and spatial arrangement of urban structures, significantly affects urban microclimate in terms of thermal environments, wind dynamics, energy use, and outdoor air quality. Despite extensive research in this field, these effects are intensified by climate change and rapid urbanization, posing challenges to urban sustainability, such as poor air quality, increased energy demands, and pedestrian discomfort. While artificial intelligence (AI) and machine learning (ML) offer promising solutions for addressing these challenges, the field lacks standardized approaches for implementing these technologies. By leveraging urban morphology indicators such as sky view factor, building density, and green space ratio, AI models can analyze complex interactions across various spatiotemporal scales. However, significant variability in methodologies, indicators, and datasets limits the generalizability and applicability of these techniques. By synthesizing 111 studies over the last decade utilizing urban morphology and AI models to predict urban microclimate, this review aims to bridge these gaps and highlight AI’s unique potential to contribute to the field. Analyzed studies reported that key urban morphology indicators, particularly building density and height, explain up to 75% of land surface temperature variance across seasons, while sky view factor accounts for over 67% of heat exposure variations in urban environments, with these findings emerging from multiple independent investigations across diverse urban contexts. Random Forest emerges as the most widely adopted AI technique, demonstrating robust performance across different applications. Emerging trends, such as hybrid approaches combining AI with physics-based models, are highlighted as promising avenues for advancing the field. Our review identifies the need for standardized frameworks and datasets to enhance model applicability. The study presents actionable insights for climate-responsive urban planning and lays the groundwork for interdisciplinary studies, enabling the development of resilient, sustainable urban environments amid the growing challenges of urbanization and climate change.http://www.sciencedirect.com/science/article/pii/S2590252025000352Urban MorphologyUrban MicroclimateArtificial IntelligenceMachine LearningClimate ChangeUrban Heat Island |
| spellingShingle | Ahmed Marey Jiwei Zou Sherif Goubran Liangzhu Leon Wang Abhishek Gaur Urban morphology impacts on urban microclimate using artificial intelligence – a review City and Environment Interactions Urban Morphology Urban Microclimate Artificial Intelligence Machine Learning Climate Change Urban Heat Island |
| title | Urban morphology impacts on urban microclimate using artificial intelligence – a review |
| title_full | Urban morphology impacts on urban microclimate using artificial intelligence – a review |
| title_fullStr | Urban morphology impacts on urban microclimate using artificial intelligence – a review |
| title_full_unstemmed | Urban morphology impacts on urban microclimate using artificial intelligence – a review |
| title_short | Urban morphology impacts on urban microclimate using artificial intelligence – a review |
| title_sort | urban morphology impacts on urban microclimate using artificial intelligence a review |
| topic | Urban Morphology Urban Microclimate Artificial Intelligence Machine Learning Climate Change Urban Heat Island |
| url | http://www.sciencedirect.com/science/article/pii/S2590252025000352 |
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