Limits of a prediction model using RNN with four layers LSTM for CRDS data

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dc.contributor.authorMEDEIROS, PEDRO A.pt_BR
dc.contributor.authorMARQUES, MARCIApt_BR
dc.contributor.authorLANDULFO, EDUARDOpt_BR
dc.coverageInternacionalpt_BR
dc.creator.eventoWORKSHOP ON LIDAR MEASUREMENTS IN LATIN AMERICA, 11thpt_BR
dc.date.accessioned2022-10-06T15:31:53Z
dc.date.available2022-10-06T15:31:53Z
dc.date.eventoOctober 19-22, 2021pt_BR
dc.description.abstractPrediction models can be very useful when dealing with ciclic temporal sequence of data. For ciclic sequences that have caotic behavior, like atmospherics measures, the Recurrent Neural Network(RNN) is a potential option to create acurate prediction models.This type of neural network is effective in dealing with temporal sequences because it uses its internal state as a memory to process certain data intervals. In building the model, it is necessary to use layers with neurons to discern a trend that the data sequence takes, for this, in the RNN model it is possible to use the long short-term memory (LSTM) architecture to more easily predict unusual behaviors in the data stream, since its use facilitates the recognition of long-term sequences in the analyzed sequence. This type of architecture can be added in layers to increase model efficiency. For the training and testing of the model, data obtained by the Metroclima project with a Cavity Ring Down Spectroscopy (CRDS) at the UNICID station located in São Paulo were used, with the data ranging from 2019 to 2021. In this process, four LSTM layers will be used to create a prediction model that will be tested to its limit on the effectiveness of predicting atmospheric data of this type.pt_BR
dc.event.siglaWLMLApt_BR
dc.format.extent57-57pt_BR
dc.identifier.citationMEDEIROS, PEDRO A.; MARQUES, MARCIA; LANDULFO, EDUARDO. Limits of a prediction model using RNN with four layers LSTM for CRDS data. In: WORKSHOP ON LIDAR MEASUREMENTS IN LATIN AMERICA, 11th, October 19-22, 2021, Punta Arenas, Chile. <b>Abstract...</b> Punta Arenas, Chile: Universidad de Magallanes, 2021. p. 57-57. Disponível em: http://repositorio.ipen.br/handle/123456789/33312.
dc.identifier.orcid0000-0002-9691-5306pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0002-9691-5306
dc.identifier.urihttp://repositorio.ipen.br/handle/123456789/33312
dc.localPunta Arenas, Chilept_BR
dc.local.eventoPunta Arenas, Chilept_BR
dc.publisherUniversidad de Magallanespt_BR
dc.rightsopenAccesspt_BR
dc.subjectneural networks
dc.subjectcavity resonators
dc.subjectcomputerized simulation
dc.titleLimits of a prediction model using RNN with four layers LSTM for CRDS datapt_BR
dc.typeResumo de eventos científicospt_BR
dspace.entity.typePublication
ipen.autorEDUARDO LANDULFO
ipen.autorPEDRO AMARAL MEDEIROS
ipen.codigoautor503
ipen.codigoautor15528
ipen.contributor.ipenauthorEDUARDO LANDULFO
ipen.contributor.ipenauthorPEDRO AMARAL MEDEIROS
ipen.date.recebimento22-10
ipen.event.datapadronizada2021pt_BR
ipen.identifier.ipendoc28968pt_BR
ipen.notas.internasAbstractpt_BR
ipen.type.genreResumo
relation.isAuthorOfPublicatione4dff370-e8c1-4437-846a-ef18a3ad606b
relation.isAuthorOfPublication09681b07-cf03-49dd-86b2-022a41140cdd
relation.isAuthorOfPublication.latestForDiscovery09681b07-cf03-49dd-86b2-022a41140cdd
sigepi.autor.atividadeLANDULFO, EDUARDO:503:920:Npt_BR
sigepi.autor.atividadeMEDEIROS, PEDRO A.:15528:920:Spt_BR
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