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Integrated management of water supply systems with efficient use of resources requires optimization of operational performances. Clustering 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. Taking into account hydraulic, operational and mathematical criteria to determine the configuration of DMAs, this work presents k-means model and a hybrid model, that combines a self-organizing map (SOM) with k-means algorithm, as clustering methods, comparing four clustering mathematical criteria, namely Silhouette, GAP, Calinski-Harabasz and Davies-Bouldin; and analyzing the influence of varying three clustering topological criteria, the water demand, node elevation and pipe length, in order to determine the optimal number of clusters. Furthermore, to identify the best DMA configuration, the particle swarm optimization (PSO) method is applied to determine the number and location of DMA entrances.

Optimal management of pressure in water supply networks through district metered areas with creation based on machine learning