Wuhan Ligong Daxue Xuebao (Jiaotong Kexue Yu Gongcheng Ban)/Journal of Wuhan University of Technology (Transportation Science and Engineering)

Urban water bodies play a critical role in mitigating the Urban Heat Island (UHI) effect, particularly in hot-arid environments where high solar radiation and low humidity intensify thermal stress. While extensive research has examined large natural water bodies, comparatively limited attention has been given to the performance optimization of artificial lakes across varying sizes and spatial configurations. Moreover, the nonlinear relationship between water-body area, cooling range, and peak temperature reduction remains insufficiently explored, particularly within compact urban contexts. This research addresses this gap by developing an integrated empirical and computational framework for evaluating and optimizing urban lake deployment. Initially, spatial cooling behavior was quantified using geostatistical analysis and remote sensing data derived from Landsat 8 imagery. The analysis identified discrete cooling performance patterns associated with different lake sizes, highlighting trade-offs between spatial coverage and localized temperature reduction. Building upon these findings, a physics-informed discrete optimization model was formulated, combining cellular spatial representation, diffusion-based thermal constraints, and artificial neural network decision mechanisms. The framework integrates environmental variables, geometric constraints, and wind alignment considerations to generate climate-responsive lake configurations. The proposed approach advances urban blue infrastructure planning from descriptive thermal assessment toward prescriptive spatial optimization, offering a scalable digital tool for climate-adaptive urban design in hot-arid cities.