IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i10p2970-d234080.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/10/2970/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/11/10/2970/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jung, Sungmoon & Arda Vanli, O. & Kwon, Soon-Duck, 2013. "Wind energy potential assessment considering the uncertainties due to limited data," Applied Energy, Elsevier, vol. 102(C), pages 1492-1503.
    2. Izabela Rojek & Jan Studzinski, 2019. "Detection and Localization of Water Leaks in Water Nets Supported by an ICT System with Artificial Intelligence Methods as a Way Forward for Smart Cities," Sustainability, MDPI, vol. 11(2), pages 1-13, January.
    3. Lingbo Liu & Zhenghong Peng & Hao Wu & Hongzan Jiao & Yang Yu & Jie Zhao, 2018. "Fast Identification of Urban Sprawl Based on K-Means Clustering with Population Density and Local Spatial Entropy," Sustainability, MDPI, vol. 10(8), pages 1-16, July.
    4. Jianrong Yao & Yanqin Pan & Shuiqing Yang & Yuangao Chen & Yixiao Li, 2019. "Detecting Fraudulent Financial Statements for the Sustainable Development of the Socio-Economy in China: A Multi-Analytic Approach," Sustainability, MDPI, vol. 11(6), pages 1-17, March.
    5. Ifaei, Pouya & Farid, Alireza & Yoo, ChangKyoo, 2018. "An optimal renewable energy management strategy with and without hydropower using a factor weighted multi-criteria decision making analysis and nation-wide big data - Case study in Iran," Energy, Elsevier, vol. 158(C), pages 357-372.
    6. Dongwoo Jang & Hyoseon Park & Gyewoon Choi, 2018. "Estimation of Leakage Ratio Using Principal Component Analysis and Artificial Neural Network in Water Distribution Systems," Sustainability, MDPI, vol. 10(3), pages 1-13, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Amirinia, Gholamreza & Mafi, Somayeh & Mazaheri, Said, 2017. "Offshore wind resource assessment of Persian Gulf using uncertainty analysis and GIS," Renewable Energy, Elsevier, vol. 113(C), pages 915-929.
    2. Xinxin Liu & Xiaosheng Wang & Haiying Guo & Xiaojie An, 2021. "Benefit Allocation in Shared Water-Saving Management Contract Projects Based on Modified Expected Shapley Value," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(1), pages 39-62, January.
    3. Liang, Yushi & Wu, Chunbing & Ji, Xiaodong & Zhang, Mulan & Li, Yiran & He, Jianjun & Qin, Zhiheng, 2022. "Estimation of the influences of spatiotemporal variations in air density on wind energy assessment in China based on deep neural network," Energy, Elsevier, vol. 239(PC).
    4. Aliashim Albani & Mohd Zamri Ibrahim & Kim Hwang Yong, 2018. "Influence of the ENSO and Monsoonal Season on Long-Term Wind Energy Potential in Malaysia," Energies, MDPI, vol. 11(11), pages 1-18, November.
    5. Allison Lassiter & Nicole Leonard, 2022. "A systematic review of municipal smart water for climate adaptation and mitigation," Environment and Planning B, , vol. 49(5), pages 1406-1430, June.
    6. Ritter, Matthias & Shen, Zhiwei & López Cabrera, Brenda & Odening, Martin & Deckert, Lars, 2015. "Designing an index for assessing wind energy potential," Renewable Energy, Elsevier, vol. 83(C), pages 416-424.
    7. Hwangbo, Soonho & Heo, SungKu & Yoo, ChangKyoo, 2022. "Development of deterministic-stochastic model to integrate variable renewable energy-driven electricity and large-scale utility networks: Towards decarbonization petrochemical industry," Energy, Elsevier, vol. 238(PC).
    8. Zhang, Jing & Lei, Xiaohui & Chen, Bin & Song, Yongyu, 2019. "Analysis of blue water footprint of hydropower considering allocation coefficients for multi-purpose reservoirs," Energy, Elsevier, vol. 188(C).
    9. Ifaei, Pouya & Tayerani Charmchi, Amir Saman & Loy-Benitez, Jorge & Yang, Rebecca Jing & Yoo, ChangKyoo, 2022. "A data-driven analytical roadmap to a sustainable 2030 in South Korea based on optimal renewable microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    10. Roberto Magini & Manuela Moretti & Maria Antonietta Boniforti & Roberto Guercio, 2023. "A Machine-Learning Approach for Monitoring Water Distribution Networks (WDNs)," Sustainability, MDPI, vol. 15(4), pages 1-17, February.
    11. Deep, Sneh & Sarkar, Arnab & Ghawat, Mayur & Rajak, Manoj Kumar, 2020. "Estimation of the wind energy potential for coastal locations in India using the Weibull model," Renewable Energy, Elsevier, vol. 161(C), pages 319-339.
    12. Tariq Judeh & Isam Shahrour & Fadi Comair, 2022. "Smart Rainwater Harvesting for Sustainable Potable Water Supply in Arid and Semi-Arid Areas," Sustainability, MDPI, vol. 14(15), pages 1-22, July.
    13. Xiangzhou Chen & Zhi Long, 2023. "E-Commerce Enterprises Financial Risk Prediction Based on FA-PSO-LSTM Neural Network Deep Learning Model," Sustainability, MDPI, vol. 15(7), pages 1-17, March.
    14. Mohamed S. Hashish & Hany M. Hasanien & Haoran Ji & Abdulaziz Alkuhayli & Mohammed Alharbi & Tlenshiyeva Akmaral & Rania A. Turky & Francisco Jurado & Ahmed O. Badr, 2023. "Monte Carlo Simulation and a Clustering Technique for Solving the Probabilistic Optimal Power Flow Problem for Hybrid Renewable Energy Systems," Sustainability, MDPI, vol. 15(1), pages 1-25, January.
    15. Bossavy, Arthur & Girard, Robin & Kariniotakis, Georges, 2016. "Sensitivity analysis in the technical potential assessment of onshore wind and ground solar photovoltaic power resources at regional scale," Applied Energy, Elsevier, vol. 182(C), pages 145-153.
    16. Farid Antonio Barrozo Budes & Guillermo Valencia Ochoa & Luis Guillermo Obregon & Adriana Arango-Manrique & José Ricardo Núñez Álvarez, 2020. "Energy, Economic, and Environmental Evaluation of a Proposed Solar-Wind Power On-grid System Using HOMER Pro ® : A Case Study in Colombia," Energies, MDPI, vol. 13(7), pages 1-19, April.
    17. Murthy, K.S.R. & Rahi, O.P., 2017. "A comprehensive review of wind resource assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 1320-1342.
    18. Mariia Pankova & Aleksy Kwilinski & Nataliya Dalevska & Valentyna Khobta, 2023. "Modelling the Level of the Enterprise’ Resource Security Using Artificial Neural Networks," Virtual Economics, The London Academy of Science and Business, vol. 6(1), pages 71-91, March.
    19. Wang, Peng & Li, Yanting & Zhang, Guangyao, 2023. "Probabilistic power curve estimation based on meteorological factors and density LSTM," Energy, Elsevier, vol. 269(C).
    20. Ifaei, Pouya & Nazari-Heris, Morteza & Tayerani Charmchi, Amir Saman & Asadi, Somayeh & Yoo, ChangKyoo, 2023. "Sustainable energies and machine learning: An organized review of recent applications and challenges," Energy, Elsevier, vol. 266(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:11:y:2019:i:10:p:2970-:d:234080. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.