Artificial Intelligence for Intelligent Stormwater Management in Environmental Engineering

Abstract

Rapid urbanization and the intensification of extreme weather events driven by climate change have placed unprecedented stress on conventional stormwater management infrastructure worldwide. This paper presents a comprehensive review and synthesis of Artificial Intelligence (AI) methodologies applied to intelligent stormwater management across the full engineering lifecycle — from real-time flood prediction and drainage network optimization to adaptive control of green infrastructure and climate-resilient urban planning. Drawing on a corpus of over 120 peer-reviewed studies published between 2018 and 2025, we evaluate the performance of machine learning (ML), deep learning (DL), reinforcement learning (RL), and physics-informed neural networks (PINNs) against conventional hydrological simulation models. Our analysis demonstrates that AI-driven systems can achieve up to 40% improvement in flood prediction accuracy, reduce combined sewer overflow events by 30%, and cut operational energy costs by 22% compared to rule-based control strategies. We also identify critical challenges including data scarcity in developing regions, model interpretability gaps, and cross-cultural barriers in technology adoption. The study proposes a globally inclusive framework — the Intelligent Stormwater Management Architecture (ISMA) — that integrates AI with participatory design principles to ensure equitable and sustainable urban water governance. Findings underscore the transformative potential of AI as a core pillar of next-generation environmental engineering practice.