Generative Water-Sensitive Spatial Configuration: AI-Driven Design, Modeling, and Adaptive Urban Hydrology

Dear Colleagues,

Rapid urbanization, climate flux, and increasing hydrological uncertainty have exposed the limitations of conventional rule-based and static approaches to water-sensitive urban design; instead, recent advances in generative artificial intelligence, spatial computing, and data-driven modeling now offer new opportunities to rethink how water-sensitive spaces are configured, optimized, and adapted across scales.

Consequently, this Special Issue focuses on generative water-sensitive spatial configuration, emphasizing the integration of generative algorithms, urban hydrological modeling, and nature-based solutions to support adaptive stormwater management and resilient urban landscapes. By coupling generative design frameworks (e.g., GANs, evolutionary algorithms, and procedural modeling) with hydrological simulation, remote sensing, and multi-objective optimization, emerging approaches enable the exploration of diverse spatial configurations that balance flood mitigation, ecosystem services, and urban functionality under uncertain climate futures.

We welcome contributions that advance theoretical frameworks, computational methods, and real-world applications addressing the co-evolution of urban form, blue–green infrastructure, and hydrological processes. Topics may span from neighborhood-scale water-sensitive design generation to city-scale adaptive planning strategies informed by AI-driven scenario exploration. By bridging generative design and urban hydrology, this Special Issue aims to promote a new paradigm of adaptive, data-informed, and performance-driven water-sensitive spatial planning.

Dr. Mo Wang
Dr. Rana Muhammad Adnan Ikram
Guest Editors

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