This paper presents a practical approach to address the challenges of managing water supply infrastructure in the context of sustainability amidst growing demands for fresh water, infrastructure deterioration, and global water shortages. Specifically, it prescribes an approach for effectively partitioning water distribution networks (WDNs) into district metered areas (DMAs) leveraging graph theoretic algorithms and the NSGA-II multi-objective optimization strategy. The dynamic layouts of DMAs are optimized based on the number of flow meters and gate valves, while reducing computational effort. This approach also minimizes variable installation costs and enhances water loss monitoring and management. Notably, it allows flexible modifications to the number of flow meters and gate valves, avoiding the need for a complete DMA layout overhaul in response to changes in hydraulic criteria of the network. The effectiveness of this approach is demonstrated through successful implementation on six complex benchmark WDNs, each comprising hundreds of pipes and nodes, offering a cost-effective means for water utilities to establish sustainable DMAs for efficient management. [Google Scholar] [Springer Link]

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Considering the scarcity of water resources, it is necessary to identify the leakage in Water Distribution Networks (WDNs). In this paper, a step-by-step method of WDN decomposition has been introduced for leak detection. First, the WDN is divided into two parts using the graph theory, then the part with leakage is identified using the results of pressure loggers and the artificial neural network. This process continues for the identified part to reach the limited leakage area. This method was applied to the Balerma WDN with five leakage scenarios including uncertainty of demand and pressure parameters. The results show that the proposed method can find the leakage area of WDNs with good accuracy. [Google Scholar] [Taylor & Francis Link]

One of the advantages of designing water distribution networks (WDNs) as a district metered areas (DMAs) is to identify the leakage in each area by controlling the input and output flow, which of course requires the separating areas and installation of flowmeters between the interconnect pipes of areas. Considering that the most existing WDNs have been expanded traditionally and not as DMA, turning them into DMAs would require huge costs and might not be even practical in some networks. In this paper, a theoretical idea of virtual DMA is presented to identify the leakage in each areas. The innovation of this paper is the ability to transform networks into DMAs using a combination of the graph theory and artificial neural network to find leaks without using a flowmeter. The proposed method, in addition to reducing costs for the flowmeters, also increases the speed of detection of leakage areas. In addition, there is no need to specify the number of leakage nodes before the leak operation begins. The proposed method has been applied for the Balerma WDN in Spain with 443 nodes and 454 pipes for two, three and four simultaneous leaks. The results of this paper show that the proposed theory in this method is able to detect leakage in each area, and this method can determine the number of optimal virtual DMA for each network. In all examples, the leakage area was correctly predicted and the maximum leakage error was about 6.5%. [Google Scholar]

Considering the scarcity of water resources, it is necessary to identify the leakage in Water Distribution Networks (WDNs). In this paper, a step-by-step method of WDN decomposition has been introduced for leak detection. First, the WDN is divided into two parts using the graph theory, then the part with leakage is identified using the results of pressure loggers and the artificial neural network. This process continues for the identified part to reach the limited leakage area. This method was applied to the Balerma WDN with five leakage scenarios including uncertainty of demand and pressure parameters. The results show that the proposed method can find the leakage area of WDNs with good accuracy. [Google Scholar]