Integrating generative AI and climate modeling for urban heat island mitigation
Conventional urban heat island (UHI) studies often rely on static urban morphology inputs and oversimplified design variables, limiting their ability to support dynamic, climate-responsive urban planning. To address this gap, this study proposes a novel framework that integrates a hybrid generative...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-12-01
|
| Series: | Ecological Informatics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954125002936 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849233608951201792 |
|---|---|
| author | Mo Wang Ziheng Xiong Shiqi Zhou Jiayu Zhao Chuanhao Sun Yuankai Wang Lie Wang Soon Keat Tan |
| author_facet | Mo Wang Ziheng Xiong Shiqi Zhou Jiayu Zhao Chuanhao Sun Yuankai Wang Lie Wang Soon Keat Tan |
| author_sort | Mo Wang |
| collection | DOAJ |
| description | Conventional urban heat island (UHI) studies often rely on static urban morphology inputs and oversimplified design variables, limiting their ability to support dynamic, climate-responsive urban planning. To address this gap, this study proposes a novel framework that integrates a hybrid generative adversarial network (GAN) with the Urban Weather Generator (UWG) for high-fidelity 3D urban form generation and microclimate simulation. The proposed GAN architecture combines the geometric accuracy of Pix2pix with the style refinement capability of CycleGAN, achieving improved morphologicalrealism (SSIM = 0.754, R2 = 0.834 against ground-truth data) and resolving key distortions that impede microclimate analysis. Applied Shenzhen Bay Super Headquarters as a case study, ten urban development plans were generated and evaluated for their thermal performance. Results revealed that plans exceeding a facade-to-site ratio of 5.0 and footprint density of 0.30 showed intensified nocturnal heat retention, with Plan V exhibiting a + 2.3 °C nighttime temperature increase. In contrast, Plan I, with lower morphological density, achieved a 1.8 °C reduction, demonstrating superior heat dissipation. These insights provide actionable guidelines for climate-responsive urban planning, advocating for lower-density layouts with optimized facade exposure and increased vegetative cover. The proposed framework offers a robust tool for planners and policymakers to assess and design urban forms that enhance climate resilience while reducing UHI intensity. |
| format | Article |
| id | doaj-art-9f23bdb7b0be4a3aa937b68de08a7170 |
| institution | Kabale University |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-9f23bdb7b0be4a3aa937b68de08a71702025-08-20T05:05:33ZengElsevierEcological Informatics1574-95412025-12-019010328410.1016/j.ecoinf.2025.103284Integrating generative AI and climate modeling for urban heat island mitigationMo Wang0Ziheng Xiong1Shiqi Zhou2Jiayu Zhao3Chuanhao Sun4Yuankai Wang5Lie Wang6Soon Keat Tan7College of Architecture and Urban Planning, Guangzhou University, Guangzhou 510006, China; Corresponding authors.College of Architecture and Urban Planning, Guangzhou University, Guangzhou 510006, ChinaCollege of Design and Innovation, Tongji University, Shanghai 200093, China; Corresponding authors.College of Architecture and Urban Planning, Guangzhou University, Guangzhou 510006, ChinaCollege of Architecture and Urban Planning, Guangzhou University, Guangzhou 510006, ChinaDepartment of Urban Planning and Design, The University of Hong Kong, Pok Fu Lam, Hong Kong, ChinaArt School, Hunan University of Information Technology, Changsha 410151, ChinaSchool of Civil and Environmental Engineering, Nanyang Technological University, Singapore 639798, SingaporeConventional urban heat island (UHI) studies often rely on static urban morphology inputs and oversimplified design variables, limiting their ability to support dynamic, climate-responsive urban planning. To address this gap, this study proposes a novel framework that integrates a hybrid generative adversarial network (GAN) with the Urban Weather Generator (UWG) for high-fidelity 3D urban form generation and microclimate simulation. The proposed GAN architecture combines the geometric accuracy of Pix2pix with the style refinement capability of CycleGAN, achieving improved morphologicalrealism (SSIM = 0.754, R2 = 0.834 against ground-truth data) and resolving key distortions that impede microclimate analysis. Applied Shenzhen Bay Super Headquarters as a case study, ten urban development plans were generated and evaluated for their thermal performance. Results revealed that plans exceeding a facade-to-site ratio of 5.0 and footprint density of 0.30 showed intensified nocturnal heat retention, with Plan V exhibiting a + 2.3 °C nighttime temperature increase. In contrast, Plan I, with lower morphological density, achieved a 1.8 °C reduction, demonstrating superior heat dissipation. These insights provide actionable guidelines for climate-responsive urban planning, advocating for lower-density layouts with optimized facade exposure and increased vegetative cover. The proposed framework offers a robust tool for planners and policymakers to assess and design urban forms that enhance climate resilience while reducing UHI intensity.http://www.sciencedirect.com/science/article/pii/S1574954125002936Urban heat islandGenerative AIUrban morphologyClimate-resilient designMicroclimate modeling |
| spellingShingle | Mo Wang Ziheng Xiong Shiqi Zhou Jiayu Zhao Chuanhao Sun Yuankai Wang Lie Wang Soon Keat Tan Integrating generative AI and climate modeling for urban heat island mitigation Ecological Informatics Urban heat island Generative AI Urban morphology Climate-resilient design Microclimate modeling |
| title | Integrating generative AI and climate modeling for urban heat island mitigation |
| title_full | Integrating generative AI and climate modeling for urban heat island mitigation |
| title_fullStr | Integrating generative AI and climate modeling for urban heat island mitigation |
| title_full_unstemmed | Integrating generative AI and climate modeling for urban heat island mitigation |
| title_short | Integrating generative AI and climate modeling for urban heat island mitigation |
| title_sort | integrating generative ai and climate modeling for urban heat island mitigation |
| topic | Urban heat island Generative AI Urban morphology Climate-resilient design Microclimate modeling |
| url | http://www.sciencedirect.com/science/article/pii/S1574954125002936 |
| work_keys_str_mv | AT mowang integratinggenerativeaiandclimatemodelingforurbanheatislandmitigation AT zihengxiong integratinggenerativeaiandclimatemodelingforurbanheatislandmitigation AT shiqizhou integratinggenerativeaiandclimatemodelingforurbanheatislandmitigation AT jiayuzhao integratinggenerativeaiandclimatemodelingforurbanheatislandmitigation AT chuanhaosun integratinggenerativeaiandclimatemodelingforurbanheatislandmitigation AT yuankaiwang integratinggenerativeaiandclimatemodelingforurbanheatislandmitigation AT liewang integratinggenerativeaiandclimatemodelingforurbanheatislandmitigation AT soonkeattan integratinggenerativeaiandclimatemodelingforurbanheatislandmitigation |