Application of Artificial Neural Networks (ANNs) in the Gap Filling of Meteorological Time Series
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Revista Brasileira de Meteorologia (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0102-77862018000200317 |
Resumo: | Abstract This study estimates and fills real flaws in a series of meteorological data belonging to four regions of the state of Rio de Janeiro. For this, an Artificial Neural Network (ANN) of Multilayer Perceptron (MLP) was applied. In order to evaluate its adequacy, the monthly variables of maximum air temperature and relative humidity of the period between 05/31/2002 and 12/31/2014 were estimated and compared with the results obtained by Multiple Linear Regression (MLR) and Regions Average (RA), and still faced with the recorded data. To analyze the estimated values and define the best model for filling, statistical techniques were applied such as correlation coefficient (r), Mean Percentage Error (MPE) and others. The results showed a high relation with the recorded data, presenting indexes between 0.94 to 0.98 of (r) for maximum air temperature and between 2.32% to 1.05% of (MPE), maintaining the precision between 97% A 99%. For the relative air humidity, the index (r) with MLP remained between 0.77 and 0.94 and (MPE) between 2.41% and 1.85%, maintaining estimates between 97% and 98%. These results highlight MLP as being effective in estimating and filling missing values. |
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Application of Artificial Neural Networks (ANNs) in the Gap Filling of Meteorological Time Seriesfault fillingmeteorological dataArtificial Neural Network (ANN)Multilayer Perceptron (MLP)Multiple Linear Regression (MLR)Abstract This study estimates and fills real flaws in a series of meteorological data belonging to four regions of the state of Rio de Janeiro. For this, an Artificial Neural Network (ANN) of Multilayer Perceptron (MLP) was applied. In order to evaluate its adequacy, the monthly variables of maximum air temperature and relative humidity of the period between 05/31/2002 and 12/31/2014 were estimated and compared with the results obtained by Multiple Linear Regression (MLR) and Regions Average (RA), and still faced with the recorded data. To analyze the estimated values and define the best model for filling, statistical techniques were applied such as correlation coefficient (r), Mean Percentage Error (MPE) and others. The results showed a high relation with the recorded data, presenting indexes between 0.94 to 0.98 of (r) for maximum air temperature and between 2.32% to 1.05% of (MPE), maintaining the precision between 97% A 99%. For the relative air humidity, the index (r) with MLP remained between 0.77 and 0.94 and (MPE) between 2.41% and 1.85%, maintaining estimates between 97% and 98%. These results highlight MLP as being effective in estimating and filling missing values.Sociedade Brasileira de Meteorologia2018-06-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0102-77862018000200317Revista Brasileira de Meteorologia v.33 n.2 2018reponame:Revista Brasileira de Meteorologia (Online)instname:Sociedade Brasileira de Meteorologia (SBMET)instacron:SBMET10.1590/0102-7786332013info:eu-repo/semantics/openAccessCoutinho,Eluã RamosSilva,Robson Mariano daMadeira,Jonni Guiller FerreiraCoutinho,Pollyanna Rodrigues de Oliveira dos SantosBoloy,Ronney Arismel ManceboDelgado,Angel Ramon Sanchezeng2019-05-27T00:00:00Zoai:scielo:S0102-77862018000200317Revistahttp://www.rbmet.org.br/port/index.phpONGhttps://old.scielo.br/oai/scielo-oai.php||rbmet@rbmet.org.br1982-43510102-7786opendoar:2019-05-27T00:00Revista Brasileira de Meteorologia (Online) - Sociedade Brasileira de Meteorologia (SBMET)false |
dc.title.none.fl_str_mv |
Application of Artificial Neural Networks (ANNs) in the Gap Filling of Meteorological Time Series |
title |
Application of Artificial Neural Networks (ANNs) in the Gap Filling of Meteorological Time Series |
spellingShingle |
Application of Artificial Neural Networks (ANNs) in the Gap Filling of Meteorological Time Series Coutinho,Eluã Ramos fault filling meteorological data Artificial Neural Network (ANN) Multilayer Perceptron (MLP) Multiple Linear Regression (MLR) |
title_short |
Application of Artificial Neural Networks (ANNs) in the Gap Filling of Meteorological Time Series |
title_full |
Application of Artificial Neural Networks (ANNs) in the Gap Filling of Meteorological Time Series |
title_fullStr |
Application of Artificial Neural Networks (ANNs) in the Gap Filling of Meteorological Time Series |
title_full_unstemmed |
Application of Artificial Neural Networks (ANNs) in the Gap Filling of Meteorological Time Series |
title_sort |
Application of Artificial Neural Networks (ANNs) in the Gap Filling of Meteorological Time Series |
author |
Coutinho,Eluã Ramos |
author_facet |
Coutinho,Eluã Ramos Silva,Robson Mariano da Madeira,Jonni Guiller Ferreira Coutinho,Pollyanna Rodrigues de Oliveira dos Santos Boloy,Ronney Arismel Mancebo Delgado,Angel Ramon Sanchez |
author_role |
author |
author2 |
Silva,Robson Mariano da Madeira,Jonni Guiller Ferreira Coutinho,Pollyanna Rodrigues de Oliveira dos Santos Boloy,Ronney Arismel Mancebo Delgado,Angel Ramon Sanchez |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Coutinho,Eluã Ramos Silva,Robson Mariano da Madeira,Jonni Guiller Ferreira Coutinho,Pollyanna Rodrigues de Oliveira dos Santos Boloy,Ronney Arismel Mancebo Delgado,Angel Ramon Sanchez |
dc.subject.por.fl_str_mv |
fault filling meteorological data Artificial Neural Network (ANN) Multilayer Perceptron (MLP) Multiple Linear Regression (MLR) |
topic |
fault filling meteorological data Artificial Neural Network (ANN) Multilayer Perceptron (MLP) Multiple Linear Regression (MLR) |
description |
Abstract This study estimates and fills real flaws in a series of meteorological data belonging to four regions of the state of Rio de Janeiro. For this, an Artificial Neural Network (ANN) of Multilayer Perceptron (MLP) was applied. In order to evaluate its adequacy, the monthly variables of maximum air temperature and relative humidity of the period between 05/31/2002 and 12/31/2014 were estimated and compared with the results obtained by Multiple Linear Regression (MLR) and Regions Average (RA), and still faced with the recorded data. To analyze the estimated values and define the best model for filling, statistical techniques were applied such as correlation coefficient (r), Mean Percentage Error (MPE) and others. The results showed a high relation with the recorded data, presenting indexes between 0.94 to 0.98 of (r) for maximum air temperature and between 2.32% to 1.05% of (MPE), maintaining the precision between 97% A 99%. For the relative air humidity, the index (r) with MLP remained between 0.77 and 0.94 and (MPE) between 2.41% and 1.85%, maintaining estimates between 97% and 98%. These results highlight MLP as being effective in estimating and filling missing values. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-06-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0102-77862018000200317 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0102-77862018000200317 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/0102-7786332013 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Sociedade Brasileira de Meteorologia |
publisher.none.fl_str_mv |
Sociedade Brasileira de Meteorologia |
dc.source.none.fl_str_mv |
Revista Brasileira de Meteorologia v.33 n.2 2018 reponame:Revista Brasileira de Meteorologia (Online) instname:Sociedade Brasileira de Meteorologia (SBMET) instacron:SBMET |
instname_str |
Sociedade Brasileira de Meteorologia (SBMET) |
instacron_str |
SBMET |
institution |
SBMET |
reponame_str |
Revista Brasileira de Meteorologia (Online) |
collection |
Revista Brasileira de Meteorologia (Online) |
repository.name.fl_str_mv |
Revista Brasileira de Meteorologia (Online) - Sociedade Brasileira de Meteorologia (SBMET) |
repository.mail.fl_str_mv |
||rbmet@rbmet.org.br |
_version_ |
1752122085782585344 |