Exploring spatial and temporal importance of input features and the explainability of machine learning-based modelling of water distribution systems

Abstract

Ensuring safe drinking water necessitates advanced management and monitoring techniques for water quality in distribution systems. This study leverages machine learning (ML) to model chlorine decay in a water distribution system (WDS) in British Columbia, Canada. A four-layer long short term memory (LSTM) network was trained to predict chlorine concentrations at a reservoir >24,000 m from the treatment plant. Explainable AI (XAI) techniques were applied to the trained network to address critical issues, such as enhancing the transparency and reliability of ML models. Several XAI methods were used to investigate the importance of sensor placement, identify the most significant features, understand feature ranges that result in poor performance, and validate model logic. Results demonstrated that for ML-based WDS control, sensor location is not critical, with high prediction accuracy achieved (mean absolute error <0.025 mg/L) even when exclusively using data from nodes spatially distant from the prediction site. XAI techniques showed the capability of identifying essential features and demonstrated that the behaviour of the ML model conformed with the expectations of chlorine behaviour. Superfluous variables were ranked low in importance, and the model learned fundamental aspects of chemical kinetics, such as temperature dependence and decay rate. Most importantly, the XAI methods applied showed the capability to communicate the reasoning for specific predictions, even at a local or sample-specific level. This study underscores the importance of transparency and trust in ML models, especially as the field transitions towards digital twin and Internet of Things (IoT) technologies, to enhance the effective management of water quality systems.