Automated sub-zoning of water distribution systems. (March 2015)

• Record Type: Journal Article
• Authors: Sela Perelman, Lina; Allen, Michael; Preis, Ami; Iqbal, Mudasser; Whittle, Andrew J.
• Is Part Of: Environmental modelling & software. Volume 65(2015:Mar.)
• Page Start: 1
• Page End: 14
• Publication Date: 2015-03
• Subjects: Water distribution systems -- Community structure -- Graph clustering and partitioning; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2014.11.025
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Water distribution systems (WDS) are complex pipe networks with looped and branching topologies that often comprise thousands to tens of thousands of links and nodes. This work presents a generic framework for improved analysis and management of WDS by partitioning the system into smaller (almost) independent sub-systems with balanced loads and minimal number of interconnections. This paper compares the performance of three classes of unsupervised learning algorithms from graph theory for practical sub-zoning of WDS: (1) Global clustering – a bottom-up algorithm for clustering n objects with respect to a similarity function, (2) Community structure – a bottom-up algorithm based on the property of network modularity, which is a measure of the quality of network partition to clusters versus randomly generated graph with respect to the same nodal degree, and (3) Graph partitioning – a flat partitioning algorithm for dividing a network with n nodes into k clusters, such that the total weight of edges crossing between clusters is minimized and the loads of all the clusters are balanced. The algorithms are adapted to WDS to provide a practical decision support tool for water utilities. Visual qualitative and quantitative measures are proposed to evaluate models' performance. The three methods are applied for two large-scale water distribution systems serving heavily populated areas in Singapore. Graphical abstract: Highlights: We apply three classes of unsupervised learning algorithms for WDS partition. Visual and quantitative metrics are used for performance evaluation and comparison. We provide a practical decision support tool for water utilities. Results show superiority of the community structure and graph partitioning methods.