Deep Learning Frameworks for Real-Time Stormwater Flow and Contaminant Prediction

Abstract

Stormwater systems in urban and industrial environments are highly dynamic, with flow rates and contaminant concentrations fluctuating rapidly during rainfall events. Traditional modeling approaches are often unable to capture these nonlinear, time-dependent variations in real time. Deep learning (DL) frameworks have emerged as a powerful solution for predicting stormwater flow and contaminant dynamics due to their ability to learn complex temporal and spatial patterns from large datasets. This study explores the application of deep learning models, including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and hybrid CNN-LSTM architectures, for real-time prediction of stormwater flow and pollutant concentrations. Model performance was evaluated using metrics such as R², RMSE, and MAE. Results demonstrate that LSTM-based models outperform conventional machine learning techniques in capturing temporal dependencies, while hybrid models provide the highest overall predictive accuracy. The integration of real-time sensor data significantly enhances forecasting reliability, enabling early warning and adaptive stormwater management. The findings highlight deep learning as a transformative approach for intelligent, real-time water quality prediction systems.