Water care through gamification : a discussion about its implementability

Carreño-Alvarado, Elizabeth Pauline; Reynoso-Meza, Gilberto; Medina, Ismael Orozco

doi:10.3850/iahr-39wc2521711920221730

Cited by 1 publication

(1 citation statement)

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“…Similarly, data-driven methodologies have been developed to rely less on hydraulic models. On the one hand, the use of classifiers such as those based on k-NN, Bayesian and discriminant analysis methods have been explored in [21][22][23][24]. In [25], a statistical classifier with a finite impulse response (FIR) filter, which was added to improve the classification results, is presented.…”

Section: Introductionmentioning

confidence: 99%

Two-Leak Isolation in Water Distribution Networks Based on k-NN and Linear Discriminant Classifiers

Rodríguez-Argote,

Begovich-Mendoza,

Navarro-Díaz

et al. 2023

Water

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In this paper, the two-simultaneous-leak isolation problem in water distribution networks is addressed. This methodology relies on optimal sensor placement together with a leak location strategy using two well-known classifiers: k-NN and discriminant analysis. First, zone segmentation of the water distribution network is proposed, aiming to reduce the computational cost that involves all possible combinations of two-leak scenarios. Each zone is composed of at least two consecutive nodes, which means that the number of zones is at most half the number of nodes. With this segmentation, the leak identification task is to locate the zones where the pair of leaks are occurring. To quantify the uncertainty degree, a relaxation node criterion is used. The simulation results evidenced that the outcomes are accurate in most cases by using one-relaxation-node and two-relaxation-node criteria.

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