Two-Leak Isolation in Water Distribution Networks Based on k-NN and Linear Discriminant Classifiers
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 d...
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| Veröffentlicht in: | Water (Basel) 2023-09, Vol.15 (17), p.3090 |
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| creator | Rodríguez-Argote, Carlos Andrés Begovich-Mendoza, Ofelia Navarro-Díaz, Adrián Santos-Ruiz, Ildeberto Puig, Vicenç Delgado-Aguiñaga, Jorge Alejandro |
| description | 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. |
| doi_str_mv | 10.3390/w15173090 |
| format | Article |
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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. 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| subjects | Algorithms Artificial intelligence Discriminant analysis Leak detection Methods Neural networks Sensors uncertainty water water distribution |
| title | Two-Leak Isolation in Water Distribution Networks Based on k-NN and Linear Discriminant Classifiers |
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