McMillan, Lauren;
Fayaz, Jawad;
Varga, Liz;
(2023)
Flow Forecasting for Leakage Burst Prediction in Water Distribution Systems using Long Short-Term Memory Neural Networks and Kalman Filtering.
Sustainable Cities and Society
, 99
, Article 104934. 10.1016/j.scs.2023.104934.
Preview |
Text
1-s2.0-S2210670723005450-main.pdf Download (9MB) | Preview |
Abstract
Reducing pipe leakage is one of the top priorities for water companies, with many investing in higher quality sensor coverage to improve flow forecasting and detection of leaks. Most research on this topic is focused on leakage detection through the analysis of sensor data from district metered areas (DMAs), aiming to identify bursts after their occurrence. This study is a step towards the development of ‘self-healing’ water infrastructure systems. In particular, machine learning and deep learning-based algorithms are applied to forecasting the anomalous water flow experienced during bursts (new leakage) in DMAs at various temporal scales, thereby aiding in the health monitoring of water distribution systems. This study uses a dataset for over 2,000 DMAs in Yorkshire, containing flow time series recorded at 15-minute intervals over one year. Firstly, the method of isolation forests is used to identify anomalies in the dataset, which are verified as corresponding to entries in the water mains repair log, indicating the occurrence of bursts. Going beyond leakage detection, this research proposes a hybrid deep learning framework named FLUIDS (Forecasting Leakage and Usual flow Intelligently in Distribution Systems). A recurrent neural network (RNN) is used for mean flow forecasting, which is then combined with forecasted residuals obtained through real-time Kalman filter. While providing expected day-to-day flow demands, this framework also aims to issue sufficient early warning for any upcoming anomalous flow or possible leakages. For a given forecast period, the FLUIDS 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 that best utilize resources to minimize leakage and disruptions by addressing detected and predicted burst events. The proposed FLUIDS framework is statistically assessed and compared against state-of-practice minimum night flow (MNF) methodology. Finally, it is concluded that the framework performs well on the unobserved test dataset for both regular and leakage water flows.
| Type: | Article |
|---|---|
| Title: | Flow Forecasting for Leakage Burst Prediction in Water Distribution Systems using Long Short-Term Memory Neural Networks and Kalman Filtering |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.scs.2023.104934 |
| Publisher version: | https://doi.org/10.1016/j.scs.2023.104934 |
| Language: | English |
| Additional information: | Copyright © 2023 The Author(s). This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
| Keywords: | Water flow forecasting, leakage prediction, long short-term memory, recurrent neural networks, Kalman filter, self-healing systems |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10177305 |
Archive Staff Only
 |
View Item |
