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A comparison of machine learning classifiers for leak detection and isolation in urban networks

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A comparison of machine learning classifiers for leak detection and isolation in urban networks

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dc.contributor.author Carreño-Alvarado, Elizabeth P. es_ES
dc.contributor.author Reynoso-Meza, Gilberto es_ES
dc.contributor.author Montalvo, Idel es_ES
dc.contributor.author Izquierdo Sebastián, Joaquín es_ES
dc.date.accessioned 2021-02-09T12:52:06Z
dc.date.available 2021-02-09T12:52:06Z
dc.date.issued 2017-07-05 es_ES
dc.identifier.isbn 978-84-947311-0-5 es_ES
dc.identifier.uri http://hdl.handle.net/10251/160954
dc.description.abstract [Otros] Leak detection and isolation (LDI) is a problem of interest for water management companies and their technical staff. Main reasons for this are that early detection of leakages can reduce dramatically (1) water losses in urban networks and (2) the environmental burden due to wasted energy used in the system supply [1]. Water leakage can become a very complex problem, due to the lack of information about the water system and because a leak might not be easily detected on-sight. Therefore, any diagnostic tool that could help in such task are valuable for engineers and managers. Soft computing tools have shown to be valuable tools for researchers in different fields. Supervised machine learning techniques for example, have been used with success in complex problems, for binary and multi class classification. This is useful in order to detect different faulty scenarios in complex systems using for example, on-line data from SCADA systems. In this paper, we provide an analysis on some soft computing techniques used for LDI in urban networks. This with the aim of identifying strengths and drawbacks among different machine learning techniques for this task in real-time acquisition scenarios. es_ES
dc.description.sponsorship The first author acknowledges SEMNI for providing registration fees for this conference. The second author would like to acknowledge the National Council of Scientific and Technological Development of Brazil (CNPq) for providing funding through the grant PQ-2/304066/2016-8. es_ES
dc.language Otros es_ES
dc.publisher International Center for Numerical Methods in Engineering (CIMNE) es_ES
dc.relation CNPq/PQ-2/304066/2016-8 es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Leak detection es_ES
dc.subject Machine learning es_ES
dc.subject Water distribution system es_ES
dc.subject Urban network es_ES
dc.subject Hanoi network es_ES
dc.subject.classification INGENIERIA DE SISTEMAS Y AUTOMATICA es_ES
dc.subject.classification MATEMATICA APLICADA es_ES
dc.title A comparison of machine learning classifiers for leak detection and isolation in urban networks es_ES
dc.type Comunicación en congreso es_ES
dc.type Capítulo de libro es_ES
dc.rights.accessRights Cerrado es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería de Sistemas y Automática - Departament d'Enginyeria de Sistemes i Automàtica es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Matemática Aplicada - Departament de Matemàtica Aplicada es_ES
dc.description.bibliographicCitation Carreño-Alvarado, EP.; Reynoso-Meza, G.; Montalvo, I.; Izquierdo Sebastián, J. (2017). A comparison of machine learning classifiers for leak detection and isolation in urban networks. International Center for Numerical Methods in Engineering (CIMNE). 1545-1552. http://hdl.handle.net/10251/160954 es_ES
dc.description.accrualMethod S es_ES
dc.relation.conferencename Congreso de Métodos Numéricos en Ingeniería (CMN 2017) es_ES
dc.relation.conferencedate Julio 03-05,2017 es_ES
dc.relation.conferenceplace Valencia, Spain es_ES
dc.description.upvformatpinicio 1545 es_ES
dc.description.upvformatpfin 1552 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.relation.pasarela S\340841 es_ES
dc.contributor.funder Conselho Nacional de Desenvolvimento Científico e Tecnológico, Brasil es_ES


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