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Near Real-time Leak Location by Inverse Analysis Integrating Measurement Uncertainty

Author

Listed:
  • Bruno Ferreira

    (INCITE, Instituto Politécnico de Setúbal)

  • Nelson Carriço

    (INCITE, Instituto Politécnico de Setúbal)

  • Dídia Covas

    (CERIS, Universidade de Lisboa)

Abstract

This paper presents a novel model-based method for near real-time pipe burst location in water distribution networks by integrating measurement uncertainty into inverse analysis. The method accounts for expected errors between measured and computed values, providing a pipe burst location area whose size varies according to the expected error level and the burst size. The proposed method is demonstrated and compared with the traditional inverse approach using a real case study with artificial bursts of different sizes and with different pressure signal noise levels. The performance of both methods is also assessed and discussed considering the effect of seasonal water demands. The traditional inverse analysis fails to accurately locate the pipe burst events, and depending on the expected error level and pipe burst size, the obtained locations may be significantly further away from the real burst location. Conversely, the proposed method does not point to the exact burst location but provides an approximated area in which step-testing can be carried out to pinpoint the exact burst location; the size of this area can be larger or smaller depending on the burst flow rate and signal uncertainty.

Suggested Citation

  • Bruno Ferreira & Nelson Carriço & Dídia Covas, 2025. "Near Real-time Leak Location by Inverse Analysis Integrating Measurement Uncertainty," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 39(1), pages 503-521, January.
    • Handle: RePEc:spr:waterr:v:39:y:2025:i:1:d:10.1007_s11269-024-03983-w
    DOI: 10.1007/s11269-024-03983-w
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Reza Moasheri & Mohammadreza Jalili-Ghazizadeh, 2020. "Locating of Probabilistic Leakage Areas in Water Distribution Networks by a Calibration Method Using the Imperialist Competitive Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(1), pages 35-49, January.
    4. Irene Marzola & Stefano Alvisi & Marco Franchini, 2022. "A Comparison of Model-Based Methods for Leakage Localization in Water Distribution Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(14), pages 5711-5727, November.
    5. Juan Li & Wenjun Zheng & Changgang Lu, 2022. "An Accurate Leakage Localization Method for Water Supply Network Based on Deep Learning Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(7), pages 2309-2325, May.
    6. Erfan Hajibandeh & Sara Nazif, 2018. "Pressure Zoning Approach for Leak Detection in Water Distribution Systems Based on a Multi Objective Ant Colony Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(7), pages 2287-2300, May.
    7. Sanghoon Jun & Kevin E. Lansey, 2023. "Convolutional Neural Network for Burst Detection in Smart Water Distribution Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(9), pages 3729-3743, July.
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