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dc.contributor.authorAniket N. Sharma
dc.contributor.authorShilpa R. Dongre
dc.contributor.authorRajesh Gupta
dc.contributor.authorLindell Ormsbee
dc.date.accessioned2022-08-18T12:32:47Z
dc.date.available2022-08-18T12:32:47Z
dc.date.issued2022/05/30
dc.identifier.other%28ASCE%29WR.1943-5452.0001586.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4286789
dc.description.abstractThe formation of district metered areas (DMAs) is an efficient strategy for the operation and management of water distribution networks (WDNs). Identifying the most suitable DMA layout is a challenging task for water utilities as it may involve several aspects that need to be addressed simultaneously. This study presents a novel multiphase approach for optimal DMA design that involves: (1) a combination of a fast Newman algorithm (FNA) to identify initial clusters; (2) a nondominated sorting genetic algorithm (NSGA-III) to obtain a set of good DMA configurations while considering several objectives simultaneously; and (3) a multiple attribute decision-making method (MADM) to find the best suited DMA configuration from a set of feasible alternative solutions based on the preference given to each objective. The proposed methodology is applied to two networks including a large benchmark network and a real-life water network. Four problem objectives out of several possible objectives were considered. These are: (1) the total cost of implementation (economic criterion); (2) the pressure deviation (hydraulic criterion); (3) a resilience index (energy criterion); and (4) the total demand shortfall (customer satisfaction criterion). Finally, a multiple attribute decision-making tool [i.e., a simple additive weighing (SAW) method] was used to arrive at a unique solution out of a set of feasible solutions. Results show that the proposed methodology can effectively identify DMAs while considering multiple objectives.
dc.publisherASCE
dc.titleMultiphase Procedure for Identifying District Metered Areas in Water Distribution Networks Using Community Detection, NSGA-III Optimization, and Multiple Attribute Decision Making
dc.typeJournal Article
dc.journal.volume148
dc.journal.issue8
dc.journal.titleJournal of Water Resources Planning and Management
dc.identifier.doi10.1061/(ASCE)WR.1943-5452.0001586
dc.journal.fristpage04022040
dc.journal.lastpage04022040-11
dc.page11
dc.treeJournal of Water Resources Planning and Management:;2022:;Volume ( 148 ):;issue: 008
dc.contenttypeFulltext


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