Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/73847
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dc.contributor.authorRodríguez-Esparragón, Dionisioen_US
dc.contributor.authorMarcello, Javieren_US
dc.contributor.authorMarrero Betancort, Nereaen_US
dc.contributor.authorGonzalo-Martin, Consueloen_US
dc.date.accessioned2020-07-28T10:35:15Z-
dc.date.available2020-07-28T10:35:15Z-
dc.date.issued2019en_US
dc.identifier.isbn9781728109671en_US
dc.identifier.otherScopus-
dc.identifier.urihttp://hdl.handle.net/10553/73847-
dc.description.abstractGlobal change is one of the outstanding problems nowadays. This is the reason why considerable attention, and economic resources to monitor climate variables have increased. Wind data constitute one of the key elements that determine the local climate. In this paper, the performance of a shallow neural net (SNN) is tested to simulate remote sensing wind intensity data from reanalysis data from nearby location. As a result, a sequence of wind data with more spatial resolution can be achieved, allowing the availability of more data at the local scale.en_US
dc.languageengen_US
dc.sourceIWOBI 2019 - IEEE International Work Conference on Bioinspired Intelligence, Proceedings, p. 87-92, (Julio 2019)en_US
dc.subject2502 Climatologíaen_US
dc.subject.otherGlobal Changeen_US
dc.subject.otherPredictionen_US
dc.subject.otherShallow Neural Neten_US
dc.subject.otherSNNen_US
dc.subject.otherWind Intensityen_US
dc.titleEstimation of wind intensity data from reanalysis data using a shallow neural networken_US
dc.typeinfo:eu-repo/semantics/conferenceObjecten_US
dc.typeConferenceObjecten_US
dc.relation.conference2019 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2019en_US
dc.identifier.doi10.1109/IWOBI47054.2019.9114455en_US
dc.identifier.scopus85087278306-
dc.contributor.authorscopusid56422496000-
dc.contributor.authorscopusid6602158797-
dc.contributor.authorscopusid57211808247-
dc.contributor.authorscopusid36561411500-
dc.description.lastpage92en_US
dc.description.firstpage87en_US
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Actas de congresosen_US
dc.utils.revisionen_US
dc.date.coverdateJulio 2019en_US
dc.identifier.conferenceidevents121841-
dc.identifier.ulpgces
item.fulltextSin texto completo-
item.grantfulltextnone-
crisitem.author.deptIOCAG: Procesado de Imágenes y Teledetección-
crisitem.author.deptIU de Oceanografía y Cambio Global-
crisitem.author.deptDepartamento de Señales y Comunicaciones-
crisitem.author.deptIOCAG: Procesado de Imágenes y Teledetección-
crisitem.author.deptIU de Oceanografía y Cambio Global-
crisitem.author.deptDepartamento de Señales y Comunicaciones-
crisitem.author.orcid0000-0002-4542-2501-
crisitem.author.orcid0000-0002-9646-1017-
crisitem.author.parentorgIU de Oceanografía y Cambio Global-
crisitem.author.parentorgIU de Oceanografía y Cambio Global-
crisitem.author.fullNameRodríguez Esparragón, Dionisio-
crisitem.author.fullNameMarcello Ruiz, Francisco Javier-
crisitem.author.fullNameMarrero Betancort, Nerea-
crisitem.author.fullNameGonzalo Martin,Consuelo-
crisitem.event.eventsstartdate22-10-2019-
crisitem.event.eventsenddate25-10-2019-
Appears in Collections:Actas de congresos
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