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Assessment of data-driven modeling strategies for water delivery canals

2013-09Journal article Restricted access
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dc.contributor.authorTavares, Isaias
dc.contributor.authorBorges, José
dc.contributor.authorGonçalves Cavaco Mendes, Mário José
dc.contributor.authorAyala Botto, Miguel
dc.date.accessioned2014-09-23T09:26:14Z
dc.date.available2014-09-23T09:26:14Z
dc.date.issued2013-09
dc.description.abstractThe aim of this paper is to develop models for experimental open-channel water delivery systems and assess the use of three data-driven modeling tools toward that end. Water delivery canals are nonlinear dynamical systems and thus should be modeled to meet given operational requirements while capturing all relevant dynamics, including transport delays. Typically, the derivation of first principle models for open-channel systems is based on the use of Saint-Venant equations for shallow water, which is a time-consuming task and demands for specific expertise. The present paper proposes and assesses the use of three data-driven modeling tools: artificial neural networks, composite local linear models and fuzzy systems. The canal from Hydraulics and Canal Control Nucleus (A parts per thousand vora University, Portugal) will be used as a benchmark: The models are identified using data collected from the experimental facility, and then their performances are assessed based on suitable validation criterion. The performance of all models is compared among each other and against the experimental data to show the effectiveness of such tools to capture all significant dynamics within the canal system and, therefore, provide accurate nonlinear models that can be used for simulation or control. The models are available upon request to the authors.por
dc.identifier.citationTAVARES, Isaías, [et al] - Assessment of data-driven modeling strategies for water delivery canals. Neural Computing and Applications. Vol. 23, nr. 3-4 (2013), p. 625-633.por
dc.identifier.doi10.1007/s00521-013-1417-8
dc.identifier.issn0941-0643
dc.identifier.urihttp://hdl.handle.net/10400.21/3834
dc.language.isoengen
dc.peerreviewedyespor
dc.publisherSpringerpor
dc.relation.ispartofseriesSI
dc.relation.publisherversionhttp://link.springer.com/article/10.1007%2Fs00521-013-1417-8por
dc.subjectNonlinear Modelingpor
dc.subjectOpen-Channel Water Delivery Systemspor
dc.subjectArtificial Neural Networkspor
dc.subjectComposite Local Linear Modelspor
dc.subjectFuzzy Systemspor
dc.titleAssessment of data-driven modeling strategies for water delivery canalspor
dc.typejournal article
dspace.entity.typePublication
oaire.citation.conferencePlaceNew Yorkpor
oaire.citation.endPage633por
oaire.citation.issue3-4por
oaire.citation.startPage625por
oaire.citation.titleNeural Computing and Applicationspor
oaire.citation.volume23por
person.familyNameGonçalves Cavaco Mendes
person.familyNameAyala Botto
person.givenNameMário José
person.givenNameMiguel
person.identifier.ciencia-idBD18-DE28-4610
person.identifier.orcid0000-0002-2448-8667
person.identifier.orcid0000-0002-9416-3892
person.identifier.ridB-5405-2008
person.identifier.ridD-4211-2016
person.identifier.scopus-author-id24340460600
person.identifier.scopus-author-id55662851100
rcaap.rightsrestrictedAccesspor
rcaap.typearticlepor
relation.isAuthorOfPublication80ad87b5-0a3b-46dd-8815-adb45f9a2e9a
relation.isAuthorOfPublication40c74d13-a61d-49bd-8e71-8b973a9865b9
relation.isAuthorOfPublication.latestForDiscovery80ad87b5-0a3b-46dd-8815-adb45f9a2e9a

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