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首页> 外文期刊>Journal of Water Resources Planning and Management >Multiphase Procedure for Identifying District Metered Areas in Water Distribution Networks Using Community Detection, NSGA-III Optimization, and Multiple Attribute Decision Making
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Multiphase Procedure for Identifying District Metered Areas in Water Distribution Networks Using Community Detection, NSGA-III Optimization, and Multiple Attribute Decision Making

机译:Multiphase Procedure for Identifying District Metered Areas in Water Distribution Networks Using Community Detection, NSGA-III Optimization, and Multiple Attribute Decision Making

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Abstract The 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.

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