A data-driven model for the prediction of chlorine losses in water distribution trunk mains

Abstract A data-driven model that uses 4 different machine learning (ML) algorithms (Feed forward artificial neural networks (ANN), Nonlinear autoregressive exogeneous (NARX) ANN, support vector and Random Forest) was designed for the prediction of chlorine loss events in water distribution trunk mains. The model, firstly, identifies past their associate flow or temperature events. Then, detected are used to train ML algorithms. tested 3 mains same drinking system with similar diameter but characteristics, using each time a combination parameters (flow (input) - losses (output) flow, temperature, (output)) Results indicate could predict future event period between 2 10 hours depending on parameter algorithm mains’ hydraulic characteristics.