AI Based Predictive Modeling for Industrial Stormwater Quality Assessment

Abstract

Industrial stormwater runoff is characterized by highly variable pollutant loads influenced by rainfall intensity, land use, industrial activities, and seasonal variations. Accurate prediction of stormwater quality is essential for effective management and pollution control; however, conventional monitoring approaches are often limited by high costs, temporal gaps, and delayed analysis. Artificial Intelligence (AI)-based predictive modeling has emerged as a powerful tool for real-time assessment and forecasting of stormwater quality by leveraging large datasets and complex nonlinear relationships. This study explores the application of machine learning algorithms, including Artificial Neural Networks (ANN), Random Forest (RF), and Support Vector Machines (SVM), for predicting key water quality parameters such as heavy metals, nutrients, chemical oxygen demand (COD), and total suspended solids (TSS) in industrial catchments. Model performance was evaluated using statistical indicators such as R², RMSE, and MAE. Results indicate that AI-based models achieve high predictive accuracy, with ANN and RF outperforming traditional regression methods. The integration of real-time sensor data further enhances model reliability and enables proactive decision-making. The findings highlight the potential of AI-driven predictive systems as efficient, cost-effective, and scalable solutions for industrial stormwater quality assessment and management.