Publication
Optimized design of neural networks for a river water level prediction system
dc.contributor.author | Lineros, Miriam LĆ³pez | |
dc.contributor.author | Luna, Antonio MadueƱo | |
dc.contributor.author | Ferreira, Pedro M. | |
dc.contributor.author | Ruano, Antonio | |
dc.date.accessioned | 2021-11-05T13:44:12Z | |
dc.date.available | 2021-11-05T13:44:12Z | |
dc.date.issued | 2021-10 | |
dc.description.abstract | In 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 Ć 10ā3 , which compares favorably with results obtained by alternative design. | pt_PT |
dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
dc.identifier.doi | 10.3390/s21196504 | pt_PT |
dc.identifier.eissn | 1424-8220 | |
dc.identifier.uri | http://hdl.handle.net/10400.1/17287 | |
dc.language.iso | eng | pt_PT |
dc.peerreviewed | yes | pt_PT |
dc.publisher | MDPI | pt_PT |
dc.relation | Associate Laboratory of Energy, Transports and Aeronautics | |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
dc.subject | Multi-objective genetic algorithm | pt_PT |
dc.subject | Artificial neural networks | pt_PT |
dc.subject | River stage data | pt_PT |
dc.title | Optimized design of neural networks for a river water level prediction system | pt_PT |
dc.title.alternative | Design otimizado de redes neurais para um sistema de previsĆ£o do nĆvel da Ć”gua do rio | pt_PT |
dc.type | journal article | |
dspace.entity.type | Publication | |
oaire.awardTitle | Associate Laboratory of Energy, Transports and Aeronautics | |
oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50022%2F2020/PT | |
oaire.citation.issue | 19 | pt_PT |
oaire.citation.startPage | 6504 | pt_PT |
oaire.citation.title | Sensors | pt_PT |
oaire.citation.volume | 21 | pt_PT |
oaire.fundingStream | 6817 - DCRRNI ID | |
person.familyName | Ruano | |
person.givenName | Antonio | |
person.identifier.orcid | 0000-0002-6308-8666 | |
person.identifier.rid | B-4135-2008 | |
person.identifier.scopus-author-id | 7004284159 | |
project.funder.identifier | http://doi.org/10.13039/501100001871 | |
project.funder.name | FundaĆ§Ć£o para a CiĆŖncia e a Tecnologia | |
rcaap.rights | openAccess | pt_PT |
rcaap.type | article | pt_PT |
relation.isAuthorOfPublication | 13813664-b68b-40aa-97a9-91481a31ebf2 | |
relation.isAuthorOfPublication.latestForDiscovery | 13813664-b68b-40aa-97a9-91481a31ebf2 | |
relation.isProjectOfPublication | 9df77b70-8231-47e7-9b34-c702e9c6021c | |
relation.isProjectOfPublication.latestForDiscovery | 9df77b70-8231-47e7-9b34-c702e9c6021c |
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