Development of an LSTM-based methodology for burst detection in water distribution systems

Abstract

Water utilities face many challenges, including pipe bursts that cause significant non-revenue water losses. Detecting those bursts early is important for the water sector in its path to achieve sustainable water resource management. This study presents a scalable data-driven methodology for burst detection in water distribution systems that is based on Long Short-Term Memory (LSTM)-based neural networks (NNs) and includes two stages: prediction and classification. Time-series of hydraulic (flow and pressure) signals are fed to the LSTM, whereas domain (time) features of the next time step are fed independently to regular neurons. These two streams of information are then concatenated to predict the values of the hydraulic features of the next time step. The model is trained on normal conditions only, so that when fed with data corresponding to a burst, the predictions will mismatch the observations. Comparison of the predictions to the observations is quantified though an error function, which is then used for classification. Specifically, a variable error threshold that corresponds to a pre-defined extreme percentile of the error distribution is used to discern bursts from normal conditions. The methodology is corroborated on two different types of bursts: (a) real bursts in district metered areas (DMAs) in the United Kingdom and (b) simulated fire hydrant leak tests in the same DMAs. For the real bursts, sensitivity analysis of the algorithm is performed to assess how data resolution and error threshold affect the performance. The flexibility of the method is studied for the simulated fire hydrant leaks, where additional information streams from new sensors are incorporated in the model by means of applying transfer learning and fine-tuning. The results obtained demonstrate that this scalable LSTM-based methodology works reasonably well in real-life settings and can successfully identify burst events, both real and simulated, even in DMAs with a small number of installed sensors. Furthermore, it is assessed how the flexibility of the LSTM neurons is pivotal for burst detection when utilizing a varying number of sensors.