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Leak Prediction Model for Water Distribution Networks Created Using a Bayesian Network Learning Approach

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  • Sou-Sen Leu

    (National Taiwan University of Science and Technology)

  • Quang-Nha Bui

    (National Taiwan University of Science and Technology)

Abstract

Water leakage in water distribution systems (WDSs) can bring various negative economic, environmental, and safety effects. Therefore, predicting water leakage is one of the most crucial tasks in water resource management; however, it is also one of the most challenging ones. Previous leakage-related studies have only focused on detecting existing leaks. This paper presents a novel model using expert structural expectation–maximisation, for predicting water leakage in WDSs. The model can take into account the uncertainty of leakage-related factors and balance the contribution of monitoring data and prior information in a Bayesian learning process to maximise leakage prediction accuracy. Moreover, the proposed method can indicate the most crucial factors affecting water leakage. The results of this study could benefit water utilities by aiding them in establishing an effective leakage control plan to minimise the risk of water leakage. A case study is presented to demonstrate the robustness and effectiveness of the proposed method.

Suggested Citation

  • Sou-Sen Leu & Quang-Nha Bui, 2016. "Leak Prediction Model for Water Distribution Networks Created Using a Bayesian Network Learning Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(8), pages 2719-2733, June.
    • Handle: RePEc:spr:waterr:v:30:y:2016:i:8:d:10.1007_s11269-016-1316-8
    DOI: 10.1007/s11269-016-1316-8
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    References listed on IDEAS

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    Cited by:

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    2. Caspar V. C. Geelen & Doekle R. Yntema & Jaap Molenaar & Karel J. Keesman, 2019. "Monitoring Support for Water Distribution Systems based on Pressure Sensor Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(10), pages 3339-3353, August.
    3. Prabal Das & D. A. Sachindra & Kironmala Chanda, 2022. "Machine Learning-Based Rainfall Forecasting with Multiple Non-Linear Feature Selection Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(15), pages 6043-6071, December.
    4. Dayong Li & Zengchuan Dong & Liyao Shi & Jintao Liu & Zhenye Zhu & Wei Xu, 2019. "Risk Probability Assessment of Sudden Water Pollution in the Plain River Network Based on Random Discharge from Multiple Risk Sources," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(12), pages 4051-4065, September.
    5. El Hassene Ait Mokhtar & Radouane Laggoune & Alaa Chateauneuf, 2016. "Utility-Based Maintenance Optimization for Complex Water-Distribution Systems Using Bayesian Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(12), pages 4153-4170, September.
    6. Massoud Tabesh & Abbas Roozbahani & Bardia Roghani & Niousha Rasi Faghihi & Reza Heydarzadeh, 2018. "Risk Assessment of Factors Influencing Non-Revenue Water Using Bayesian Networks and Fuzzy Logic," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(11), pages 3647-3670, September.
    7. Kızılöz, Burak & Şişman, Eyüp & Oruç, Halil Nurullah, 2022. "Predicting a water infrastructure leakage index via machine learning," Utilities Policy, Elsevier, vol. 75(C).
    8. Ioan Florin VOICU & Daniel Constantin DIACONU, 2021. "Reducing city household water consumption with internet of things devices," Smart Cities International Conference (SCIC) Proceedings, Smart-EDU Hub, Faculty of Public Administration, National University of Political Studies & Public Administration, vol. 9, pages 367-377, November.

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