2019
DOI: 10.1590/2318-0331.241920180165
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Optimal pressure management in water distribution networks through district metered area creation based on machine learning

Abstract: Integrated management of water supply systems with efficient use of natural resources requires optimization of operational performances. Dividing the water supply networks into small units, so-called district metered areas (DMAs), is a strategy that allows the development of specific operational rules, responsible for improving the network performance. In this context, clustering methods congregate neighboring nodes in groups according to similar features, such as elevation or distance to the water source. Tak… Show more

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Cited by 8 publications
(4 citation statements)
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“…One important task on using clustering algorithms, such as k-means, is defining the number of clusters. In Novarini et al 29 various mathematical and engineering criteria for DMA design are evaluated. Mathematical criteria evaluate the quality of the clustering process in terms of external and internal measures.…”
Section: Clustering Process Using a Modified K-means Algorithmmentioning
confidence: 99%
“…One important task on using clustering algorithms, such as k-means, is defining the number of clusters. In Novarini et al 29 various mathematical and engineering criteria for DMA design are evaluated. Mathematical criteria evaluate the quality of the clustering process in terms of external and internal measures.…”
Section: Clustering Process Using a Modified K-means Algorithmmentioning
confidence: 99%
“…The models proposed from data mining and machine learning have become popular in the last decade [21]. Some of the developed applications using machine learning are applied to optimal pressure management and district metered area design [22], leak detection in WDSs [23][24][25][26][27], water demand estimation [28][29][30], and detection of cyberattacks, physical attacks, and contamination in WDSs [31,32].…”
Section: Introductionmentioning
confidence: 99%
“…They also employed multicriterion decision analysis to compare different solutions. Novarini et al (2019) partitioned DMAs using hybrid model and K-means for D-Town network. They considered mathematical and topological criteria.…”
Section: Introductionmentioning
confidence: 99%