Nature-Based and AI-Enabled Stormwater Management for Sustainable Urban Development

Abstract

The convergence of nature-based solutions (NbS) and artificial intelligence (AI) presents an unprecedented opportunity to transform urban stormwater management from a single-purpose drainage challenge into an integrated, adaptive, and ecologically intelligent system supporting sustainable urban development. This paper presents the first comprehensive review of hybrid NbS-AI integration strategies for urban stormwater management, synthesizing evidence from 115 peer-reviewed studies, engineering case studies, and pilot programs published between 2018 and 2025 across six continents. We examine how AI techniques — including deep learning, reinforcement learning, physics-informed neural networks, and digital twin platforms — are being coupled with nature-based infrastructure elements such as bioretention systems, constructed wetlands, urban forests, green roofs, and blue-green corridors to create stormwater systems that are simultaneously high-performing, self-optimizing, and ecologically embedded. Quantitative evidence demonstrates that NbS-AI integrated systems achieve 38–52% improvements in hydrological performance metrics over standalone NbS designs, reduce operational maintenance costs by up to 35%, and extend effective flood forecast lead times by 6–8 hours compared to conventional models. We propose the Nature-AI Urban Water Framework (NAUWF) — a five-pillar architecture for implementing NbS-AI integrated stormwater systems across diverse urban development contexts — and identify the governance, data, and capacity-building prerequisites for its global deployment. Cross-cultural dimensions of NbS-AI integration, including knowledge equity, community co-design, and adaptive governance, are examined as foundational rather than peripheral concerns for sustainable implementation.