LSTM-AE-WLDL: Unsupervised LSTM Auto-Encoders for Leak Detection and Location in Water Distribution Networks

Maryam Kammoun (), Amina Kammoun () and Mohamed Abid ()
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Maryam Kammoun: National Engineering School of Sfax, Sfax University
Amina Kammoun: National Engineering School of Sfax, Sfax University
Mohamed Abid: National Engineering School of Sfax, Sfax University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2023, vol. 37, issue 2, No 8, 746 pages

Abstract: Abstract Basically, leaks and faults in water distribution pipelines beget fairly severe water loss and affects largely its potability. For this reason, leakage detection is extremely significant for the preservation of water resources and quality. This paper introduces a novel unsupervised RNN model for leakage detection and location. The elaborated approach relies upon a multivariate LSTM autoencoder, as well as a multithresholding to monitor all water distribution network zones. A threshold for each measurement point of water distribution network is determined to identify anomaly in hydraulic data and detect leak events. Furthermore, a statistical study is conducted to estimate the leak locations’ area. Both flow and pressure data from different realistic water demands scenarios of the LeakDB benchmark are assessed. Experiment results corroborate the effectiveness and reliability of the proposed system for both data types. Detection sensitivity achieved 97% using pressure data and 100 % using flow data, with true leak zone identification for 95% of scenarios.

Keywords: Leak detection; Leak area location; Unsupervised RNN; LSTM autoencoder; LeakDB (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11269-022-03397-6

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