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A Comparison of Model-Based Methods for Leakage Localization in Water Distribution Systems

Author

Listed:
  • Irene Marzola

    (University of Ferrara)

  • Stefano Alvisi

    (University of Ferrara)

  • Marco Franchini

    (University of Ferrara)

Abstract

Model-based methods for leakage localization in water distribution systems have recently been gaining more attention. These methods identify the leakage position by comparing the measured network data with the corresponding values simulated by a hydraulic model. In this study two model-based methods already proposed in literature, one based on the Sensitivity Matrix method and the other one on the Linear Approximation method, are analysed and compared to each other. The methods are applied to the same case study network, exploiting only data provided by pressure sensors. Various analyses are undertaken in order to investigate the main critical issues tied to the two methods, i.e. a) the use of different amounts of data averaged over different time windows, b) the impact of the model’s accuracy in terms of water demands and pipe roughness, and c) the effect of the number of pressure measuring points. The results show that higher efficiency is obtained by considering the hourly averaged data all together. Moreover, the Linear Approximation method is on average 3 times more accurate than the Sensitivity Matrix when a perfect hydraulic model is used, even with a reduced number of pressure sensors. However, when a hydraulic model and/or measured data affected by errors are considered, the Sensitivity Matrix is more accurate, with an average error almost 10% lower than the Linear Approximation.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:waterr:v:36:y:2022:i:14:d:10.1007_s11269-022-03329-4
    DOI: 10.1007/s11269-022-03329-4
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. E. Pacchin & F. Gagliardi & S. Alvisi & M. Franchini, 2019. "A Comparison of Short-Term Water Demand Forecasting Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(4), pages 1481-1497, March.
    4. Juan Li & Ying Wu & Wenjun Zheng & Changgang Lu, 2021. "A Model-Based Bayesian Framework for Pipeline Leakage Enumeration and Location Estimation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(13), pages 4381-4397, October.
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