Estimates of reference evapotranspiration in the municipality of Ariquemes (RO) using neural networks GMDH-type
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Revista Brasileira de Engenharia Agrícola e Ambiental (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-43662019000500324 |
Resumo: | ABSTRACT Reference evapotranspiration is a climatological variable of great importance for water use dimensioning in irrigation methods. In order to contribute to the climatic understanding of Ariquemes, Rodônia state, Brazil, the study aims to model the behavior of the time series of reference evapotranspiration using a GMDH-type (Group Method of Data Handling) artificial neural network (ANN) and to compare it with the SARIMA (Seasonal Autoregressive Integrated Moving Average) methodology. Data from the National Institute of Meteorology - INMET, obtained at the Automatic Weather Station of Ariquemes, from January 2011 to January 2014, were used. Data analysis was performed using software R version 3.3.1 through the GMDH-type ANN package. Modeling by GMDH-type ANN led to results similar to the results of the SARIMA model, thus constituting an option to predict climatic time series. GMDH-type models with larger numbers of inputs and layers presented lowest mean square error. |
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Revista Brasileira de Engenharia Agrícola e Ambiental (Online) |
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Estimates of reference evapotranspiration in the municipality of Ariquemes (RO) using neural networks GMDH-typeclimatologytimes seriesmetaheuristicABSTRACT Reference evapotranspiration is a climatological variable of great importance for water use dimensioning in irrigation methods. In order to contribute to the climatic understanding of Ariquemes, Rodônia state, Brazil, the study aims to model the behavior of the time series of reference evapotranspiration using a GMDH-type (Group Method of Data Handling) artificial neural network (ANN) and to compare it with the SARIMA (Seasonal Autoregressive Integrated Moving Average) methodology. Data from the National Institute of Meteorology - INMET, obtained at the Automatic Weather Station of Ariquemes, from January 2011 to January 2014, were used. Data analysis was performed using software R version 3.3.1 through the GMDH-type ANN package. Modeling by GMDH-type ANN led to results similar to the results of the SARIMA model, thus constituting an option to predict climatic time series. GMDH-type models with larger numbers of inputs and layers presented lowest mean square error.Departamento de Engenharia Agrícola - UFCG2019-05-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-43662019000500324Revista Brasileira de Engenharia Agrícola e Ambiental v.23 n.5 2019reponame:Revista Brasileira de Engenharia Agrícola e Ambiental (Online)instname:Universidade Federal de Campina Grande (UFCG)instacron:UFCG10.1590/1807-1929/agriambi.v23n5p324-329info:eu-repo/semantics/openAccessCarvalho,Roberto L. da S.Delgado,Angel R. S.eng2019-05-02T00:00:00Zoai:scielo:S1415-43662019000500324Revistahttp://www.scielo.br/rbeaaPUBhttps://old.scielo.br/oai/scielo-oai.php||agriambi@agriambi.com.br1807-19291415-4366opendoar:2019-05-02T00:00Revista Brasileira de Engenharia Agrícola e Ambiental (Online) - Universidade Federal de Campina Grande (UFCG)false |
dc.title.none.fl_str_mv |
Estimates of reference evapotranspiration in the municipality of Ariquemes (RO) using neural networks GMDH-type |
title |
Estimates of reference evapotranspiration in the municipality of Ariquemes (RO) using neural networks GMDH-type |
spellingShingle |
Estimates of reference evapotranspiration in the municipality of Ariquemes (RO) using neural networks GMDH-type Carvalho,Roberto L. da S. climatology times series metaheuristic |
title_short |
Estimates of reference evapotranspiration in the municipality of Ariquemes (RO) using neural networks GMDH-type |
title_full |
Estimates of reference evapotranspiration in the municipality of Ariquemes (RO) using neural networks GMDH-type |
title_fullStr |
Estimates of reference evapotranspiration in the municipality of Ariquemes (RO) using neural networks GMDH-type |
title_full_unstemmed |
Estimates of reference evapotranspiration in the municipality of Ariquemes (RO) using neural networks GMDH-type |
title_sort |
Estimates of reference evapotranspiration in the municipality of Ariquemes (RO) using neural networks GMDH-type |
author |
Carvalho,Roberto L. da S. |
author_facet |
Carvalho,Roberto L. da S. Delgado,Angel R. S. |
author_role |
author |
author2 |
Delgado,Angel R. S. |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Carvalho,Roberto L. da S. Delgado,Angel R. S. |
dc.subject.por.fl_str_mv |
climatology times series metaheuristic |
topic |
climatology times series metaheuristic |
description |
ABSTRACT Reference evapotranspiration is a climatological variable of great importance for water use dimensioning in irrigation methods. In order to contribute to the climatic understanding of Ariquemes, Rodônia state, Brazil, the study aims to model the behavior of the time series of reference evapotranspiration using a GMDH-type (Group Method of Data Handling) artificial neural network (ANN) and to compare it with the SARIMA (Seasonal Autoregressive Integrated Moving Average) methodology. Data from the National Institute of Meteorology - INMET, obtained at the Automatic Weather Station of Ariquemes, from January 2011 to January 2014, were used. Data analysis was performed using software R version 3.3.1 through the GMDH-type ANN package. Modeling by GMDH-type ANN led to results similar to the results of the SARIMA model, thus constituting an option to predict climatic time series. GMDH-type models with larger numbers of inputs and layers presented lowest mean square error. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-05-01 |
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=S1415-43662019000500324 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-43662019000500324 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1807-1929/agriambi.v23n5p324-329 |
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 |
Departamento de Engenharia Agrícola - UFCG |
publisher.none.fl_str_mv |
Departamento de Engenharia Agrícola - UFCG |
dc.source.none.fl_str_mv |
Revista Brasileira de Engenharia Agrícola e Ambiental v.23 n.5 2019 reponame:Revista Brasileira de Engenharia Agrícola e Ambiental (Online) instname:Universidade Federal de Campina Grande (UFCG) instacron:UFCG |
instname_str |
Universidade Federal de Campina Grande (UFCG) |
instacron_str |
UFCG |
institution |
UFCG |
reponame_str |
Revista Brasileira de Engenharia Agrícola e Ambiental (Online) |
collection |
Revista Brasileira de Engenharia Agrícola e Ambiental (Online) |
repository.name.fl_str_mv |
Revista Brasileira de Engenharia Agrícola e Ambiental (Online) - Universidade Federal de Campina Grande (UFCG) |
repository.mail.fl_str_mv |
||agriambi@agriambi.com.br |
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1750297686834151424 |