Urbanization has significantly altered natural hydrological cycles, leading to increased runoff volumes and degraded water quality. Traditional "gray infrastructure" approaches face limitations in adapting to changing climatic conditions and growing urban demands. Green infrastructure (GI), which includes features like rain gardens, bioswales, and permeable pavements, offers a promising solution by mimicking natural hydrological processes. The integration of artificial intelligence (AI) into GI systems enhances their design, monitoring, and maintenance through advanced analytics and real-time data processing. This study explores the role of AI in managing urban runoff through GI, focusing on modeling, optimization, real-time monitoring, climate adaptation, and quantification of environmental co-benefits. Key findings include the high accuracy of AI-driven models in predicting runoff dynamics, significant improvements in GI design efficiency, enhanced resilience through real-time monitoring, development of climate-resilient designs, and robust assessments of co-benefits such as carbon sequestration and biodiversity enhancement. While challenges remain, such as data scarcity and computational complexity, the potential of AI in creating smarter, more sustainable urban environments is undeniable.
