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

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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)

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.

Suggested Citation

  • Maryam Kammoun & Amina Kammoun & Mohamed Abid, 2023. "LSTM-AE-WLDL: Unsupervised LSTM Auto-Encoders for Leak Detection and Location in Water Distribution Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(2), pages 731-746, January.
    • Handle: RePEc:spr:waterr:v:37:y:2023:i:2:d:10.1007_s11269-022-03397-6
    DOI: 10.1007/s11269-022-03397-6
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    References listed on IDEAS

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    1. Jing Cheng & Sen Peng & Rui Cheng & Xingqi Wu & Xu Fang, 2022. "Burst Area Identification of Water Supply Network by Improved DenseNet Algorithm with Attention Mechanism," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(14), pages 5425-5442, November.
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