Artificial Intelligence-Driven Stormwater Management: A New Paradigm in Environmental Engineering

Abstract

Environmental engineering is undergoing a paradigm shift of historic proportions. For more than a century, stormwater management has been governed by deterministic design methodologies, empirical hydrological models, and rules-based operational protocols whose fundamental assumptions — statistical stationarity of precipitation, predictable urban growth trajectories, and the sufficiency of periodic human oversight — are being systematically invalidated by climate change, digital urbanization, and the explosive growth of environmental monitoring data. Artificial intelligence is emerging not merely as an incremental improvement to existing stormwater engineering tools but as the foundation of an entirely new engineering paradigm: one characterized by continuous learning, probabilistic performance assessment, autonomous adaptive control, and the capacity to optimize across far greater system complexity than human cognition alone can manage. This paper characterizes and validates this paradigm shift through systematic review of 140 peer-reviewed studies, engineering implementations, and technical assessments published between 2019 and 2025, spanning the full spectrum of AI application in stormwater management from deep learning flood forecasting and generative AI infrastructure design to large language model public communication and federated learning cross-city knowledge transfer. We introduce the AI-Driven Stormwater Paradigm Framework (ADSPF) — a structured characterization of the defining principles, operational architectures, professional practice implications, and governance requirements of AI-era stormwater engineering — and demonstrate its application through analysis of implementations across six contrasting national contexts