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Optimized design of neural networks for a river water level prediction system

dc.contributor.authorLineros, Miriam LĆ³pez
dc.contributor.authorLuna, Antonio MadueƱo
dc.contributor.authorFerreira, Pedro M.
dc.contributor.authorRuano, Antonio
dc.date.accessioned2021-11-05T13:44:12Z
dc.date.available2021-11-05T13:44:12Z
dc.date.issued2021-10
dc.description.abstractIn this paper, a Multi-Objective Genetic Algorithm (MOGA) framework for the design of Artificial Neural Network (ANN) models is used to design 1-step-ahead prediction models of river water levels. The design procedure is a near-automatic method that, given the data at hand, can partition it into datasets and is able to determine a near-optimal model with the right topology and inputs, offering a good performance on unseen data, i.e., data not used for model design. An example using more than 11 years of water level data (593,178 samples) of the CarriĆ³n river collected at Villoldo gauge station shows that the MOGA framework can obtain low-complex models with excellent performance on unseen data, achieving an RMSE of 2.5 Ɨ 10āˆ’3 , which compares favorably with results obtained by alternative design.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.3390/s21196504pt_PT
dc.identifier.eissn1424-8220
dc.identifier.urihttp://hdl.handle.net/10400.1/17287
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relationAssociate Laboratory of Energy, Transports and Aeronautics
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectMulti-objective genetic algorithmpt_PT
dc.subjectArtificial neural networkspt_PT
dc.subjectRiver stage datapt_PT
dc.titleOptimized design of neural networks for a river water level prediction systempt_PT
dc.title.alternativeDesign otimizado de redes neurais para um sistema de previsĆ£o do nĆ­vel da Ć”gua do riopt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleAssociate Laboratory of Energy, Transports and Aeronautics
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50022%2F2020/PT
oaire.citation.issue19pt_PT
oaire.citation.startPage6504pt_PT
oaire.citation.titleSensorspt_PT
oaire.citation.volume21pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameRuano
person.givenNameAntonio
person.identifier.orcid0000-0002-6308-8666
person.identifier.ridB-4135-2008
person.identifier.scopus-author-id7004284159
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundaĆ§Ć£o para a CiĆŖncia e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication13813664-b68b-40aa-97a9-91481a31ebf2
relation.isAuthorOfPublication.latestForDiscovery13813664-b68b-40aa-97a9-91481a31ebf2
relation.isProjectOfPublication9df77b70-8231-47e7-9b34-c702e9c6021c
relation.isProjectOfPublication.latestForDiscovery9df77b70-8231-47e7-9b34-c702e9c6021c

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