Improved modularity-based approach for partition of Water Distribution Networks

被引:14
|
作者
Yao, Huaqi [1 ]
Zhang, Tuqiao [1 ]
Shao, Yu [1 ]
Yu, Tingchao [1 ]
Lima Neto, Iran E. [2 ]
机构
[1] Zhejiang Univ, Dept Civil Engn, Hangzhou, Peoples R China
[2] Univ Fed Ceara, Dept Hydraul & Environm Engn, Fortaleza, Ceara, Brazil
基金
中国国家自然科学基金;
关键词
Network partition; modified Fast-Newman algorithm; heuristic methodology; demand similarity; DISTRICT METERED AREAS; DESIGN;
D O I
10.1080/1573062X.2020.1857801
中图分类号
TV21 [水资源调查与水利规划];
学科分类号
081501 ;
摘要
The partition of complex Water Distribution Systems (WDSs) is required in order to simplify and facilitate the routine cumbersome management tasks. As a representative community detection algorithm, the Fast-Newman Algorithm (FNA) can efficiently partition the network into District Metered areas (DMAs) based on the modularity index. However, only the topological attribute is considered in the classic version. In this work, the modularity index and corresponding mergence mechanism of FNA are modified first to improve water demand similarity among DMAs; then, an optimal selection of cut positions where flow meters or gate valves will be installed is conducted to further improve water demand similarity among DMAs; finally, the inflow pipes of DMAs are optimally selected considering economy and the impact on hydraulic performance of WDSs. The proposed approach is applied to three cases and the results reveal the superiority of this method.
引用
收藏
页码:69 / 78
页数:10
相关论文
共 50 条
  • [31] Modularity-based Control Structure Selection for Process Networks: An Extension to Distributed Parameter Systems
    Kang, Lixia
    Moharir, Manjiri
    Almansoori, Ali
    Daoutidis, Prodromos
    2017 AMERICAN CONTROL CONFERENCE (ACC), 2017, : 1145 - 1150
  • [32] Brain-inspired GCN: Modularity-based Siamese simple graph convolutional networks
    Yao, Xiao
    Zhu, Huyue
    Gu, Min
    INFORMATION SCIENCES, 2024, 657
  • [33] Modularity-Based User-Centric Clustering and Resource Allocation for Ultra Dense Networks
    Lin, Yan
    Zhang, Rong
    Yang, Luxi
    Hanzo, Lajos
    IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2018, 67 (12) : 12457 - 12461
  • [34] Comparison of modularity-based approaches for nodes clustering in hypergraphs
    Poda, Veronica
    Matias, Catherine
    PEER COMMUNITY JOURNAL, 2024, 4
  • [35] An Modularity-based Overlapping Community Structure Detecting Algorithm
    Meng, Kui
    Liu, Gongshen
    Hu, Qiong
    Li, Jianhua
    2014 PROCEEDINGS OF THE IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM 2014), 2014, : 113 - 117
  • [36] Optimized Modularity-Based High Level Classification Model
    Colliri, Tiago
    Liu, Weiguang
    Zhao, Liang
    2020 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2020,
  • [37] Providing Information Resilience through Modularity-based Caching in Perturbed Information-Centric Networks
    Chai, Wei Koong
    Sourlas, Vasilis
    Pavlou, George
    2017 PROCEEDINGS OF THE 29TH INTERNATIONAL TELETRAFFIC CONGRESS (ITC 29), VOL 1, 2017, : 214 - 222
  • [38] Resiliency analysis of electric distribution networks: A new approach based on modularity concept
    Mousavizadeh, Saeed
    Bolandi, Tohid Ghanizadeh
    Haghifam, Mahmoud-Reza
    Moghimi, Mojtaba
    Lu, Junwei
    INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2020, 117
  • [39] Correcting for Granularity Bias in Modularity-Based Community Detection Methods
    Gosgens, Martijn
    van der Hofstad, Remco
    Litvak, Nelly
    ALGORITHMS AND MODELS FOR THE WEB GRAPH, WAW 2023, 2023, 13894 : 1 - 18
  • [40] Modularity-based graph clustering for analysis of gene microarray data
    Cao, Y.-C. (yccao@scut.edu.cn), 1600, South China University of Technology (41):