Sensor placement for classifier-based leak localization in water distribution networks using hybrid feature selection

This paper presents a sensor placement approach for classifier-based leak localization in water distribution networks. The proposed method is based on a hybrid feature selection algorithm that combines the use of a filter based on relevancy and redundancy with a wrapper based on genetic algorithms....

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Detalhes bibliográficos
Autores: Soldevila Coma, Adrià, Blesa Izquierdo, Joaquim|||0000-0002-5626-3753, Tornil Sin, Sebastián|||0000-0003-1799-2192, Fernández Canti, Rosa M.|||0000-0002-9381-6601, Puig Cayuela, Vicenç|||0000-0002-6364-6429
Formato: artículo
Fecha de publicación:2018
País:España
Recursos:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/130525
Acesso em linha:https://hdl.handle.net/2117/130525
https://dx.doi.org/10.1016/j.compchemeng.2017.09.002
Access Level:acceso abierto
Palavra-chave:Automatic control
Sensor placement
Leak localization
Water distribution networks
Feature selection
Genetic algorithms
Classifiers
Control automàtic
Àrees temàtiques de la UPC::Informàtica::Automàtica i control
Descrição
Resumo:This paper presents a sensor placement approach for classifier-based leak localization in water distribution networks. The proposed method is based on a hybrid feature selection algorithm that combines the use of a filter based on relevancy and redundancy with a wrapper based on genetic algorithms. This algorithm is applied to data generated by hydraulic simulation of the considered water distribution network and it determines the optimal location of a prespecified number of pressure sensors to be used by a leak localization method based on pressure models and classifiers proposed in previous works by the authors. The method is applied to a small-size simplified network (Hanoi) to better analyze its computational performance and to a medium-size network (Limassol) to demonstrate its applicability to larger real-size networks.