Machine Learning Approaches for Forecasting Pollutant Load in Urban Runoff

Abstract

Urban runoff is a major pathway for transporting pollutants such as heavy metals, nutrients, hydrocarbons, and suspended solids into receiving water bodies. Accurate forecasting of pollutant loads is essential for effective stormwater management and pollution mitigation; however, the nonlinear and dynamic nature of urban hydrological systems poses significant challenges for traditional modeling approaches. This study investigates the application of machine learning techniques—including Artificial Neural Networks (ANN), Random Forest (RF), Support Vector Machines (SVM), and Gradient Boosting (GB)—for forecasting pollutant loads in urban runoff. Models were trained using historical meteorological, hydrological, and water quality data, including rainfall intensity, runoff volume, land-use characteristics, and pollutant concentrations. Performance evaluation based on R², RMSE, and MAE indicates that ensemble methods such as RF and GB outperform conventional regression models, achieving high predictive accuracy and robustness. The integration of real-time monitoring data further enhances forecasting capability and supports proactive stormwater management. The findings demonstrate that machine learning provides a reliable and scalable framework for predicting urban runoff pollution and optimizing environmental management strategies.