Research on partition strategy of an urban water supply network based on optimized hierarchical clustering algorithm

The partitioning of the urban water supply network can significantly enhance water supply quality. Nonetheless, the bulk of the recently deployed partition approaches overlooked the question of whether the district's fluctuation regulation of flow data is consistent. When the district is modifi...

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Vydáno v:Water science & technology. Water supply Ročník 22; číslo 4; s. 4387 - 4399
Hlavní autoři: Xia, Wei, Wang, Shi, Shi, Mingjun, Xia, Qing, Jin, Wenting
Médium: Journal Article
Jazyk:angličtina
Vydáno: London IWA Publishing 01.04.2022
Témata:
Algorithms
Cluster analysis
Clustering
Energy consumption
features of time-domain
Monitoring systems
partition strategy of water supply network
Principal components analysis
rf-hc
Water quality
Water supply
water supply network
ISSN:1606-9749, 1607-0798
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Shrnutí:The partitioning of the urban water supply network can significantly enhance water supply quality. Nonetheless, the bulk of the recently deployed partition approaches overlooked the question of whether the district's fluctuation regulation of flow data is consistent. When the district is modified, it most likely leads to an increase in pressure at a node. To tackle the problem, the flow data from a city's water supply network was evaluated in this article. The random forest approach was also used to extract time-domain characteristics from flow data, and the water supply network split was optimized using the random forest-hierarchical clustering (RF-HC) strategy. Finally, the results were examined and compared. The results suggest that the RF-HC-based water supply network partition technique can better meet the aim of consistent flow changes in the district, as well as offer a theoretical foundation and technological support for the optimal dispatch of press concerning the water supply network.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1606-9749
1607-0798
DOI:10.2166/ws.2022.057