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dc.contributor.author | Ambrosio, J. K.![]() |
es_ES |
dc.contributor.author | Brentan, B. M.![]() |
es_ES |
dc.contributor.author | Herrera Fernández, Antonio Manuel![]() |
es_ES |
dc.contributor.author | Luvizotto, E.![]() |
es_ES |
dc.contributor.author | Ribeiro, L.![]() |
es_ES |
dc.contributor.author | Izquierdo Sebastián, Joaquín![]() |
es_ES |
dc.date.accessioned | 2020-04-01T07:16:04Z | |
dc.date.available | 2020-04-01T07:16:04Z | |
dc.date.issued | 2019-01-08 | es_ES |
dc.identifier.issn | 1024-123X | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/139939 | |
dc.description.abstract | [EN] Prediction models have become essential for the improvement of decision-making processes in public management and, particularly, for water supply utilities. Accurate estimation often needs to solve multimeasurement, mixed-mode, and space-time problems, typical of many engineering applications. As a result, accurate estimation of real world variables is still one of the major problems in mathematical approximation. Several individual techniques have shown very good estimation abilities. However, none of them are free from drawbacks. This paper faces the challenge of creating accurate water demand predictive models at urban scale by using so-called committee machines, which are ensemble frameworks of single machine learning models. The proposal is able to combine models of varied nature. Specifically, this paper analyzes combinations of such techniques as multilayer perceptrons, support vector machines, extreme learning machines, random forests, adaptive neural fuzzy inference systems, and the group method for data handling. Analyses are checked on two water demand datasets from Franca (Brazil). As an ensemble tool, the combined response of a committee machine outperforms any single constituent model. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Hindawi Limited | es_ES |
dc.relation.ispartof | Mathematical Problems in Engineering | es_ES |
dc.rights | Reconocimiento (by) | es_ES |
dc.subject.classification | MATEMATICA APLICADA | es_ES |
dc.title | Committee Machines for Hourly Water Demand Forecasting in Water Supply Systems | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1155/2019/9765468 | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Instituto Universitario de Matemática Multidisciplinar - Institut Universitari de Matemàtica Multidisciplinària | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Matemática Aplicada - Departament de Matemàtica Aplicada | es_ES |
dc.description.bibliographicCitation | Ambrosio, JK.; Brentan, BM.; Herrera Fernández, AM.; Luvizotto, E.; Ribeiro, L.; Izquierdo Sebastián, J. (2019). Committee Machines for Hourly Water Demand Forecasting in Water Supply Systems. Mathematical Problems in Engineering. 2019:1-11. https://doi.org/10.1155/2019/9765468 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.1155/2019/9765468 | es_ES |
dc.description.upvformatpinicio | 1 | es_ES |
dc.description.upvformatpfin | 11 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 2019 | es_ES |
dc.relation.pasarela | S\373712 | es_ES |
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