Integrating generative AI and climate modeling for urban heat island mitigation - Mo Wang; Ziheng Xiong; Shiqi Zhou; Jiayu Zhao; Chuanhao Sun; Yuankai Wang; Lie Wang; Soon Keat Tan (Ecological Informatics)
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.