AI-driven Predictive Models for Industrial Stormwater Quality and Flow

Abstract

Industrial stormwater management is increasingly challenged by highly variable runoff volumes and rapidly fluctuating pollutant loads that are difficult to capture using conventional monitoring and control approaches. Artificial intelligence driven predictive models offer a data centric solution by enabling continuous analysis of hydrological and water quality data to anticipate changes in stormwater flow and contaminant concentrations. This article examines the application of machine learning techniques for predicting pollutant spikes and flow dynamics within industrial stormwater systems and their integration with automated treatment controls. By leveraging historical records and real time sensor inputs, AI based models support proactive operational decisions, optimize treatment performance, and reduce the risk of non compliance with discharge standards. The findings highlight the potential of predictive intelligence to enhance system resilience, improve resource efficiency, and advance sustainable industrial stormwater management practices.