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An Efficient Burst Detection and Isolation Monitoring System for Water Distribution Networks Using Multivariate Statistical Techniques

Author

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  • KiJeon Nam

    (Department of Environmental Science and Engineering, College of Engineering, Kyung Hee University, Seocheon-dong 1, Giheung-gu, Yongin-si, Gyeonggi-do 446-701, Korea
    The first and second authors contributed equally to this paper.)

  • Pouya Ifaei

    (Department of Environmental Science and Engineering, College of Engineering, Kyung Hee University, Seocheon-dong 1, Giheung-gu, Yongin-si, Gyeonggi-do 446-701, Korea
    The first and second authors contributed equally to this paper.)

  • Sungku Heo

    (Department of Environmental Science and Engineering, College of Engineering, Kyung Hee University, Seocheon-dong 1, Giheung-gu, Yongin-si, Gyeonggi-do 446-701, Korea)

  • Gahee Rhee

    (Department of Environmental Science and Engineering, College of Engineering, Kyung Hee University, Seocheon-dong 1, Giheung-gu, Yongin-si, Gyeonggi-do 446-701, Korea)

  • Seungchul Lee

    (Technology Development Center, Samsung engineering, Woncheon-dong, Yeongtong-gu, Suwon-si, Gyeonggi-do 446-701, Korea)

  • ChangKyoo Yoo

    (Department of Environmental Science and Engineering, College of Engineering, Kyung Hee University, Seocheon-dong 1, Giheung-gu, Yongin-si, Gyeonggi-do 446-701, Korea)

Abstract

Detection and isolation of burst locations in water distribution networks (WDN) are challenging problems in urban management because burst events cause considerable economic, social, and environmental losses. In the present study, a novel monitoring and sensor placement approach is proposed for rapid and robust burst detection. Accordingly, a hybrid principal component analysis (PCA) and standardized exponential weighted moving average (EWMA) system is proposed for WDN monitoring and management. In addition, the optimal sensor configuration is obtained using PCA, k -means clustering, and a sensitivity analysis considering the diurnal patterns and the noises of pressure and flowrate data in the WDN. The proposed system is applied to a branched WDN, and the results are compared to those obtained with conventional monitoring systems. The results show that the proposed system detected the burst occurrence regardless of noise size with a detection rate of 93%. Compared to conventional systems, the isolation ratio improved by 10%, indicating that the bursts were isolated more accurately. In addition, the corresponding sensor configuration was 40% less expensive than the conventional systems.

Suggested Citation

  • KiJeon Nam & Pouya Ifaei & Sungku Heo & Gahee Rhee & Seungchul Lee & ChangKyoo Yoo, 2019. "An Efficient Burst Detection and Isolation Monitoring System for Water Distribution Networks Using Multivariate Statistical Techniques," Sustainability, MDPI, vol. 11(10), pages 1-17, May.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:10:p:2970-:d:234080
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    References listed on IDEAS

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    Cited by:

    1. Ryul Kim & Young Hwan Choi, 2023. "The Development of a Data-Based Leakage Pinpoint Detection Technique for Water Distribution Systems," Mathematics, MDPI, vol. 11(9), pages 1-18, May.
    2. Sehyeong Kim & Sanghoon Jun & Donghwi Jung, 2022. "Ensemble CNN Model for Effective Pipe Burst Detection 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(13), pages 5049-5061, October.
    3. 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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