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. Taking into account hydraulic, operational and mathematical criteria to determine the configuration of DMAs, this work presents the k-means model and a hybrid model, that combines a self-organizing map (SOM) with the k-means algorithm, as clustering methods, comparing four mathematical criteria to determine the number of DMAs, namely Silhouette, GAP, Calinski-Harabasz and Davies Bouldin. The influence of three clustering topological criteria is evaluated: 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 was applied to determine the number, cost, pressure setting of Pressure Reducing Valves and location of DMA entrances.
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.
Water demand forecast models are extremely important for logistic and political issues. In this sense, the present research aims to develop an Artificial Neural Network (ANN), able to predict in the short term, future water demands. Because of the high precision with time series data, the NARX (Nonlinear Autoregressive with External Input), a type of ANN, was chosen. Several tests were conducted in order to find the best forecast model, and the results showed that the NARX is an efficient predictor of water demand, requiring a good database for the ANN training phase.
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