IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v36y2022i13d10.1007_s11269-022-03312-z.html
   My bibliography  Save this article

Sensor Partitioning Placements via Random Walk and Water Quality and Leakage Detection Models within Water Distribution Systems

Author

Listed:
  • Tianwei Mu

    (Shenyang University, Ministry of Education)

  • Manhong Huang

    (State Environmental Protection Engineering Center for Pollution Treatment and Control in Textile Industry, Donghua University)

  • Shi Tang

    (Tieling Construction Engineering Construction Drawing Examination Co, Ltd.
    Shenyang Jianzhu University)

  • Rui Zhang

    (Dalian University of Technology)

  • Gang Chen

    (State Environmental Protection Engineering Center for Pollution Treatment and Control in Textile Industry, Donghua University)

  • Baiyi Jiang

    (Shenyang Jianzhu University)

Abstract

A novel sensor partitioning placement model is presented to evenly distribute sensors to water distribution systems (WDS) for monitoring leakages and contamination. First, random walk community detection (RWCD) is used to divide WDS into different partitions. Then, an extended period leakage detection (EPLD) model is presented. The total leakage detection and the average time of leakage detection are used as objective functions for pressure sensor placement. Next, the extended period water quality detection (EPWQD) model is presented. The total intrusion detection, the average percentage of clean water, and the average time of water quality detection are used as objective functions for water quality sensor placement. Evolutionary algorithm (EA) modules are applied to optimize the locations of pressure and water quality sensors. Seven networks are employed to verify the practicability of the model. The results show that leakage and intrusion detection rate is up to 85% during 24 h, and the average percentage of clean water is up to 0.9 in these cases. Finally, the model compares the leakage zone identification (LZI) and the water quality sensor placement strategy (WQSPS) models. The total detection number, the total average time of detection, and the total average percentage of clean water have been improved. Therefore, this model is a high-potential way of sensor placement. Graphical Abstract

Suggested Citation

  • Tianwei Mu & Manhong Huang & Shi Tang & Rui Zhang & Gang Chen & Baiyi Jiang, 2022. "Sensor Partitioning Placements via Random Walk and Water Quality and Leakage Detection Models within 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 5297-5311, October.
    • Handle: RePEc:spr:waterr:v:36:y:2022:i:13:d:10.1007_s11269-022-03312-z
    DOI: 10.1007/s11269-022-03312-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-022-03312-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11269-022-03312-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. LAMBIOTTE, Renaud & DELVENNE, Jean-Charles & BARAHONA, Mauricio, 2014. "Random walks, Markov processes and the multiscale modular organization of complex network," LIDAM Reprints CORE 2660, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. Tianwei Mu & Yan Lu & Haoqiang Tan & Haowen Zhang & Chengzhi Zheng, 2021. "Random Walks Partitioning and Network Reliability Assessing in Water Distribution System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(8), pages 2325-2341, June.
    Full references (including those not matched with items on IDEAS)

    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. Xin Xu & Yang Lu & Yupeng Zhou & Zhiguo Fu & Yanjie Fu & Minghao Yin, 2021. "An Information-Explainable Random Walk Based Unsupervised Network Representation Learning Framework on Node Classification Tasks," Mathematics, MDPI, vol. 9(15), pages 1-14, July.
    2. Xicheng Yin & Hongwei Wang & Pei Yin & Hengmin Zhu & Zhenyu Zhang, 2020. "A co-occurrence based approach of automatic keyword expansion using mass diffusion," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(3), pages 1885-1905, September.
    3. Xuan Khoa Bui & Gimoon Jeong & Doosun Kang, 2022. "Adaptive DMA Design and Operation under Multiscenarios in Water Distribution Networks," Sustainability, MDPI, vol. 14(6), pages 1-22, March.
    4. Tomeczek, Artur F., 2022. "The evolution of Japanese keiretsu networks: A review and text network analysis of their perceptions in economics," Japan and the World Economy, Elsevier, vol. 62(C).
    5. Benjamin Davies & David C. Maré, 2020. "Delineating functional labour market areas with estimable classification stabilities," Working Papers 20_08, Motu Economic and Public Policy Research.
    6. Artur F. Tomeczek & Tomasz M. Napiórkowski, 2024. "PageRank and Regression as a Two-Step Approach to Analysing a Network of Nasdaq Firms During a Recession: Insights from Minimum Spanning Tree Topology," Gospodarka Narodowa. The Polish Journal of Economics, Warsaw School of Economics, issue 3, pages 56-69.
    7. Yu Tian & Sebastian Lautz & Alisdiar O. G. Wallis & Renaud Lambiotte, 2021. "Extracting Complements and Substitutes from Sales Data: A Network Perspective," Papers 2103.02042, arXiv.org, revised Aug 2021.
    8. Jia, Rui & Du, Kun & Song, Zhigang & Xu, Wei & Zheng, Feifei, 2024. "Scenario reduction-based simulation method for efficient serviceability assessment of earthquake-damaged water distribution systems," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    9. Tianwei Mu & Yaqi Li & Ziyi Li & Luyue Wang & Haoqiang Tan & Chengzhi Zheng, 2021. "Improved Network Reliability Optimization Model with Head Loss for Water Distribution System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(7), pages 2101-2114, May.
    10. Arnaud Adam & Jean-Charles Delvenne & Isabelle Thomas, 2018. "Detecting communities with the multi-scale Louvain method: robustness test on the metropolitan area of Brussels," Journal of Geographical Systems, Springer, vol. 20(4), pages 363-386, October.
    11. Shamina Imran Pathan & Silvia Scibetta & Chiara Grassi & Giacomo Pietramellara & Simone Orlandini & Maria Teresa Ceccherini & Marco Napoli, 2020. "Response of Soil Bacterial Community to Application of Organic and Inorganic Phosphate Based Fertilizers under Vicia faba L. Cultivation at Two Different Phenological Stages," Sustainability, MDPI, vol. 12(22), pages 1-20, November.
    12. Leleux, Pierre & Courtain, Sylvain & Françoisse, Kevin & Saerens, Marco, 2022. "Design of biased random walks on a graph with application to collaborative recommendation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 590(C).
    13. Tianwei Mu & Yan Lu & Haoqiang Tan & Haowen Zhang & Chengzhi Zheng, 2021. "Random Walks Partitioning and Network Reliability Assessing in Water Distribution System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(8), pages 2325-2341, June.
    14. Jesus S. Alejandro-Cruz & Rosa M. Rio-Belver & Yara C. Almanza-Arjona & Alejandro Rodriguez-Andara, 2019. "Towards a Science Map on Sustainability in Higher Education," Sustainability, MDPI, vol. 11(13), pages 1-18, June.

    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:spr:waterr:v:36:y:2022:i:13:d:10.1007_s11269-022-03312-z. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.