Machine-Learning-Based Health Monitoring and Leakage Management of Water Distribution Systems




Machine Learning, Deep Learning, Leakage, Water Distribution Systems


The reduction of pipe leakage is one of the top priorities for water companies, with many investing in greater sensor coverage to improve the forecasting of flow and detection of leaks. The majority of research on this topic is focused on leakage detection through the analysis of sensor data from district metered areas (DMAs), with the aim of identifying bursts after occurrence. This study is a step towards development of ‘self-healing’ water infrastructure systems. In particular, the concepts of machine-learning (ML) and deep-learning (DL) are applied to the forecasting of water flow in DMAs at various temporal scales, thereby aiding in the health monitoring of water distribution systems.  This study uses a dataset for ~2500 DMAs in Yorkshire, containing flow time-series recorded at every 15-minute interval over the period of a year. Firstly, the method of isolation forests is used to identify anomalies in the dataset which are verified as corresponding to entries in water mains repair log, indicating the occurrence of bursts. Going beyond leakage detection, this research proposes a hybrid framework of DL models - such as recurrent neural networks (RNNs) and transformer neural networks) - and state-space ML algorithms - such as Kalman filter and autoregressive integrated moving average (ARIMA). The ML algorithms are trained to forecast the stationary component of the expected flow patterns in real-time, which is then combined (through Bayesian updating) with the non-stationary component obtained from DL models. As well as providing expected day-to-day flow demands, this framework aims to issue sufficient early warning for any upcoming anomalous flow or possible leakages. For a given forecast period, the framework can be used to compute the probability of flow exceeding a pre-defined threshold, thus allowing decisions to be made regarding any necessary interventions. This can inform targeted repair strategies which best utilise resources to minimise leakage and disruptions by addressing both detected and predicted burst events.

Author Biographies

Jawad Fayaz, Teesside University

Lecturer (Assistant Professor) in the School of Computing, Engineering & Digital Technologies at Teesside University, UK. Research work is oriented towards Civil Engineering, Computer Science, and Statistics. It includes Structural and Earthquake engineering, Seismic Ground Motion Analysis, Infrastructural Systems Analysis, Decision Support Systems for Early Warning, Hazard Analysis, and Risk and Reliability Analysis. The research projects are mainly based on Advanced Statistical Analysis, Feature Engineering, Time-Series Analysis, Machine Learning, and Deep Learning.

Liz Varga, University College London

Professor (Civil, Environmental & Geomatic Engineering)




How to Cite

McMillan, L., Fayaz, J., & Varga, L. (2023). Machine-Learning-Based Health Monitoring and Leakage Management of Water Distribution Systems . Proceedings of the International Conference on Evolving Cities, 43–50.