Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/48816
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dc.contributor.authorBeltrán-Castro, Juanen_US
dc.contributor.authorValencia-Aguirre, Julianaen_US
dc.contributor.authorOrozco-Alzate, Mauricioen_US
dc.contributor.authorCastellanos-Domínguez, Germánen_US
dc.contributor.authorTravieso-González, Carlos M.en_US
dc.date.accessioned2018-11-24T01:11:25Z-
dc.date.available2018-11-24T01:11:25Z-
dc.date.issued2013en_US
dc.identifier.isbn9783642386787en_US
dc.identifier.issn0302-9743en_US
dc.identifier.urihttp://hdl.handle.net/10553/48816-
dc.description.abstractIn this paper a methodology for rainfall forecasting is presented, using the principle of decomposition and ensemble. In the proposed framework, the employed decomposition technique is the Ensemble Empirical Mode Decomposition (EEMD), which divides the original data into a set of simple components. Each component is modeled with a Feed Forward Neural Network (FNN) as a forecasting tool. Finally, the individual forecasting results for all components are combined to obtain the prediction result of the input signal. Experiments were performed on a real-observed rainfall data, and the attained results were compared against a single FNN model for the raw data, showing an improvement on the system performance.en_US
dc.languageengen_US
dc.relation.ispartofLecture Notes in Computer Scienceen_US
dc.sourceLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)[ISSN 0302-9743],v. 7902 LNCS, p. 471-480en_US
dc.subject3307 Tecnología electrónicaen_US
dc.subject.otherForecastingen_US
dc.subject.otherNeural Networksen_US
dc.subject.otherEnsemble Empirical Mode Decompositionen_US
dc.subject.otherRainfallen_US
dc.titleRainfall forecasting based on ensemble empirical mode decomposition and neural networksen_US
dc.typeinfo:eu-repo/semantics/conferenceObjecten_US
dc.typeConferenceObjecten_US
dc.identifier.doi10.1007/978-3-642-38679-4_47en_US
dc.identifier.scopus84880053480-
dc.contributor.authorscopusid55790922600-
dc.contributor.authorscopusid36761935900-
dc.contributor.authorscopusid14623081400-
dc.contributor.authorscopusid25640642900-
dc.contributor.authorscopusid6602376272-
dc.description.lastpage480en_US
dc.description.firstpage471en_US
dc.relation.volume7902 LNCSen_US
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Actas de congresosen_US
dc.utils.revisionen_US
dc.identifier.ulpgces
dc.description.sjr0,329
dc.description.sjrqQ3
item.grantfulltextopen-
item.fulltextCon texto completo-
crisitem.author.deptGIR IDeTIC: División de Procesado Digital de Señales-
crisitem.author.deptIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.deptDepartamento de Señales y Comunicaciones-
crisitem.author.orcid0000-0002-4621-2768-
crisitem.author.parentorgIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.fullNameTravieso González, Carlos Manuel-
Colección:Actas de congresos
miniatura
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