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dc.contributor.author | Brentan, Bruno M. | es_ES |
dc.contributor.author | Meirelles, G. | es_ES |
dc.contributor.author | Luvizotto, E. | es_ES |
dc.contributor.author | Izquierdo Sebastián, Joaquín | es_ES |
dc.date.accessioned | 2019-05-11T20:04:32Z | |
dc.date.available | 2019-05-11T20:04:32Z | |
dc.date.issued | 2018 | es_ES |
dc.identifier.issn | 1364-8152 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/120366 | |
dc.description.abstract | [EN] With the advance of new technologies and emergence of the concept of the smart city, there has been a dramatic increase in available information. Water distribution systems (WDSs) in which databases can be updated every few minutes are no exception. Suitable techniques to evaluate available information and produce optimized responses are necessary for planning, operation, and management. This can help identify critical characteristics, such as leakage patterns, pipes to be replaced, and other features. This paper presents a clustering method based on self-organizing maps coupled with k-means algorithms to achieve groups that can be easily labeled and used for WDS decision-making. Three case-studies are presented, namely a classification of Brazilian cities in terms of their water utilities; district metered area creation to improve pressure control; and transient pressure signal analysis to identify burst pipes. In the three cases, this hybrid technique produces excellent results. © 2018 Elsevier Ltd. All rights reserved. | es_ES |
dc.description.sponsorship | This work is partially supported by Capes and CNPq, Brazilian research agencies. The use of English was revised by John Rawlins. | |
dc.language | Inglés | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation.ispartof | Environmental Modelling & Software | es_ES |
dc.rights | Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) | es_ES |
dc.subject | Water supply systems | es_ES |
dc.subject | Classification | es_ES |
dc.subject | Self-organized maps | es_ES |
dc.subject | K-means clustering | es_ES |
dc.subject.classification | MATEMATICA APLICADA | es_ES |
dc.title | Hybrid SOM+k-Means Clustering to Improve Planning, Operation and Management in Water Distribution Systems | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1016/j.envsoft.2018.02.013 | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Matemática Aplicada - Departament de Matemàtica Aplicada | es_ES |
dc.description.bibliographicCitation | Brentan, BM.; Meirelles, G.; Luvizotto, E.; Izquierdo Sebastián, J. (2018). Hybrid SOM+k-Means Clustering to Improve Planning, Operation and Management in Water Distribution Systems. Environmental Modelling & Software. 106:77-88. https://doi.org/10.1016/j.envsoft.2018.02.013 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | http://doi.org/10.1016/j.envsoft.2018.02.013 | es_ES |
dc.description.upvformatpinicio | 77 | es_ES |
dc.description.upvformatpfin | 88 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 106 | es_ES |
dc.relation.pasarela | S\355624 | es_ES |
dc.contributor.funder | Coordenaçao de Aperfeiçoamento de Pessoal de Nível Superior, Brasil | |
dc.contributor.funder | Conselho Nacional de Desenvolvimento Científico e Tecnológico, Brasil |