Clustering analysis of water distribution systems: identifying critical components and community impacts

被引:37
|
作者
Diao, K. [1 ]
Farmani, R. [1 ]
Fu, G. [1 ]
Astaraie-Imani, M. [1 ]
Ward, S. [1 ]
Butler, D. [1 ]
机构
[1] Univ Exeter, Ctr Water Syst, Exeter EX6 7HS, Devon, England
基金
英国工程与自然科学研究理事会;
关键词
clustering analysis; critical component; urban development; water distribution system; DECOMPOSITION;
D O I
10.2166/wst.2014.268
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Large water distribution systems (WDSs) are networks with both topological and behavioural complexity. Thereby, it is usually difficult to identify the key features of the properties of the system, and subsequently all the critical components within the system for a given purpose of design or control. One way is, however, to more explicitly visualize the network structure and interactions between components by dividing a WDS into a number of clusters (subsystems). Accordingly, this paper introduces a clustering strategy that decomposes WDSs into clusters with stronger internal connections than external connections. The detected cluster layout is very similar to the community structure of the served urban area. As WDSs may expand along with urban development in a community-by-community manner, the correspondingly formed distribution clusters may reveal some crucial configurations of WDSs. For verification, the method is applied to identify all the critical links during firefighting for the vulnerability analysis of a real-world WDS. Moreover, both the most critical pipes and clusters are addressed, given the consequences of pipe failure. Compared with the enumeration method, the method used in this study identifies the same group of the most critical components, and provides similar criticality prioritizations of them in a more computationally efficient time.
引用
收藏
页码:1764 / 1773
页数:10
相关论文
共 50 条
  • [41] Assessing impacts of distributed generation on distribution systems analysis tools
    Miu, Karen
    Carneiro, Sandoval, Jr.
    2007 IEEE POWER ENGINEERING SOCIETY GENERAL MEETING, VOLS 1-10, 2007, : 3528 - 3533
  • [42] Toward Identifying Cyber Dependencies in Water Distribution Systems Using Causal AI
    Sobien, Daniel
    Kulkarni, Ajay
    Batarseh, Feras A.
    JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT, 2025, 151 (02)
  • [43] Gated graph neural networks for identifying contamination sources in water distribution systems
    Li, Zilin
    Liu, Haixing
    Zhang, Chi
    Fu, Guangtao
    JOURNAL OF ENVIRONMENTAL MANAGEMENT, 2024, 351
  • [44] Global resilience analysis of water distribution systems
    Diao, Kegong
    Sweetapple, Chris
    Farmani, Raziyeh
    Fu, Guangtao
    Ward, Sarah
    Butler, David
    WATER RESEARCH, 2016, 106 : 383 - 393
  • [45] Integrating spatial clustering with predictive modeling of pipe failures in water distribution systems
    Abokifa, Ahmed A.
    Sela, Lina
    URBAN WATER JOURNAL, 2023, 20 (04) : 465 - 476
  • [46] Leakage localization using pressure sensors and spatial clustering in water distribution systems
    Li, Xin
    Chu, Shipeng
    Zhang, Tuqiao
    Yu, Tingchao
    Shao, Yu
    WATER SUPPLY, 2022, 22 (01) : 1020 - 1034
  • [47] Analysis and simulation of cold water distribution systems
    Beier, C
    Wigbels, M
    Hölder, D
    INDUSTRIAL ENERGY MANAGEMENT, 2001, 1593 : 525 - 537
  • [48] Modelling and reliability analysis of water distribution systems
    Tanyimboh, TT
    Burd, R
    Burrows, R
    Tabesh, M
    WATER SCIENCE AND TECHNOLOGY, 1999, 39 (04) : 249 - 255
  • [49] TRANSIENT ANALYSIS OF WATER DISTRIBUTION-SYSTEMS
    KARNEY, BW
    MCINNIS, D
    JOURNAL AMERICAN WATER WORKS ASSOCIATION, 1990, 82 (07): : 62 - 70
  • [50] Topological Observability Analysis in Water Distribution Systems
    Diaz, Sarai
    Minguez, Roberto
    Gonzalez, Javier
    JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT, 2017, 143 (05)