Using artificial neural network to estimate reference evapotranspiration
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFMG |
Texto Completo: | http://hdl.handle.net/1843/43010 |
Resumo: | Irrigation, when rationally used, can contribute to the efficient performance of the agribusiness. Planning irrigation, monitoring the soil moisture, the rainfall and the reference evapotranspiration (ET0) is necessary for a rational water management. The FAO Penman-Monteith (FAO PM) method is the standard method for estimating ET0, but in some cases, the use of this method is restricted due to missing some climatic variables. For this reason, methods with a lower number of meteorological variables, as temperature values, are quite often used. This study aims to propose an artificial neural network (ANN) to estimate the ET0 as a function of maximum and minimum air temperatures for the city of Salinas, Minas Gerais State, Brazil. After training, validation and comparison with the Hargreaves methodology, it was observed the existence of a good correlation between the values estimated by the standard method and those estimated by ANN, with the performance index classified as optimal, better than the Hargreaves methodology one. The use of ANN proved to be an excellent alternative for ET0 estimation, reducing the costs of acquiring climatic data. |
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2022-07-07T13:11:09Z2022-07-07T13:11:09Z2018-11-281132292401984-3801http://hdl.handle.net/1843/43010Irrigation, when rationally used, can contribute to the efficient performance of the agribusiness. Planning irrigation, monitoring the soil moisture, the rainfall and the reference evapotranspiration (ET0) is necessary for a rational water management. The FAO Penman-Monteith (FAO PM) method is the standard method for estimating ET0, but in some cases, the use of this method is restricted due to missing some climatic variables. For this reason, methods with a lower number of meteorological variables, as temperature values, are quite often used. This study aims to propose an artificial neural network (ANN) to estimate the ET0 as a function of maximum and minimum air temperatures for the city of Salinas, Minas Gerais State, Brazil. After training, validation and comparison with the Hargreaves methodology, it was observed the existence of a good correlation between the values estimated by the standard method and those estimated by ANN, with the performance index classified as optimal, better than the Hargreaves methodology one. The use of ANN proved to be an excellent alternative for ET0 estimation, reducing the costs of acquiring climatic data.A irrigação, sempre que utilizada de forma racional, contribui de forma importante para o desempenho do agronegócio nacional. Para um manejo racional da água de irrigação é preciso um bom planejamento das irrigações, de monitoramento da umidade do solo, das precipitações e da evapotranspiração de referência (ET0). O método Penman-Monteith FAO é o método padrão para a estimativa da evapotranspiração de referência, porém, em alguns casos, o uso do método é restrito pela ausência de algumas variáveis climáticas. Por essa razão, muitas vezes há necessidade de se calcular a ET0 empregando-se métodos que utilizem somente valores de temperatura. O objetivo deste trabalho foi propor uma rede neural artificial (RNA) para estimar a evapotranspiração de referência em função das temperaturas máxima e mínima do ar para a cidade de Salinas-MG. Após o treinamento, validação e comparação com a metodologia de Hargreaves, pode-se observar a existência de boa correlação entre os valores estimados pelo método padrão e pela RNA, além do índice de desempenho classificado como ótimo, superando a metodologia de Hargreaves. O uso da RNA mostrou-se uma excelente alternativa para a determinação da ET0, proporcionando a diminuição dos custos de aquisição de dados climáticos.engUniversidade Federal de Minas GeraisUFMGBrasilICA - INSTITUTO DE CIÊNCIAS AGRÁRIASGlobal Science and TechnologyEvapotranspiraçãoRedes neurais (Computação)Irrigação agrícolaSolos - UmidadeUsing artificial neural network to estimate reference evapotranspirationUso de rede neural artificial para a estimativa da evapotranspiração de referênciainfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttps://rv.ifgoiano.edu.br/periodicos/index.php/gst/article/view/1057Patrícia Oliveira LucasRenato Dourado MaiaMarcelo Rossi VicenteCaio Vinícius Leiteinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFMGinstname:Universidade Federal de Minas Gerais (UFMG)instacron:UFMGLICENSELicense.txtLicense.txttext/plain; charset=utf-82042https://repositorio.ufmg.br/bitstream/1843/43010/1/License.txtfa505098d172de0bc8864fc1287ffe22MD51ORIGINALUsing artificial neural network to estimate reference evapotranspiration.pdfUsing artificial neural network to estimate reference evapotranspiration.pdfapplication/pdf896631https://repositorio.ufmg.br/bitstream/1843/43010/2/Using%20artificial%20neural%20network%20to%20estimate%20reference%20evapotranspiration.pdf8e36f076c7e21571c88aae0371d90753MD521843/430102022-07-07 10:11:09.662oai:repositorio.ufmg.br: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Repositório de PublicaçõesPUBhttps://repositorio.ufmg.br/oaiopendoar:2022-07-07T13:11:09Repositório Institucional da UFMG - Universidade Federal de Minas Gerais (UFMG)false |
dc.title.pt_BR.fl_str_mv |
Using artificial neural network to estimate reference evapotranspiration |
dc.title.alternative.pt_BR.fl_str_mv |
Uso de rede neural artificial para a estimativa da evapotranspiração de referência |
title |
Using artificial neural network to estimate reference evapotranspiration |
spellingShingle |
Using artificial neural network to estimate reference evapotranspiration Patrícia Oliveira Lucas Evapotranspiração Redes neurais (Computação) Irrigação agrícola Solos - Umidade |
title_short |
Using artificial neural network to estimate reference evapotranspiration |
title_full |
Using artificial neural network to estimate reference evapotranspiration |
title_fullStr |
Using artificial neural network to estimate reference evapotranspiration |
title_full_unstemmed |
Using artificial neural network to estimate reference evapotranspiration |
title_sort |
Using artificial neural network to estimate reference evapotranspiration |
author |
Patrícia Oliveira Lucas |
author_facet |
Patrícia Oliveira Lucas Renato Dourado Maia Marcelo Rossi Vicente Caio Vinícius Leite |
author_role |
author |
author2 |
Renato Dourado Maia Marcelo Rossi Vicente Caio Vinícius Leite |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Patrícia Oliveira Lucas Renato Dourado Maia Marcelo Rossi Vicente Caio Vinícius Leite |
dc.subject.other.pt_BR.fl_str_mv |
Evapotranspiração Redes neurais (Computação) Irrigação agrícola Solos - Umidade |
topic |
Evapotranspiração Redes neurais (Computação) Irrigação agrícola Solos - Umidade |
description |
Irrigation, when rationally used, can contribute to the efficient performance of the agribusiness. Planning irrigation, monitoring the soil moisture, the rainfall and the reference evapotranspiration (ET0) is necessary for a rational water management. The FAO Penman-Monteith (FAO PM) method is the standard method for estimating ET0, but in some cases, the use of this method is restricted due to missing some climatic variables. For this reason, methods with a lower number of meteorological variables, as temperature values, are quite often used. This study aims to propose an artificial neural network (ANN) to estimate the ET0 as a function of maximum and minimum air temperatures for the city of Salinas, Minas Gerais State, Brazil. After training, validation and comparison with the Hargreaves methodology, it was observed the existence of a good correlation between the values estimated by the standard method and those estimated by ANN, with the performance index classified as optimal, better than the Hargreaves methodology one. The use of ANN proved to be an excellent alternative for ET0 estimation, reducing the costs of acquiring climatic data. |
publishDate |
2018 |
dc.date.issued.fl_str_mv |
2018-11-28 |
dc.date.accessioned.fl_str_mv |
2022-07-07T13:11:09Z |
dc.date.available.fl_str_mv |
2022-07-07T13:11:09Z |
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info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/1843/43010 |
dc.identifier.issn.pt_BR.fl_str_mv |
1984-3801 |
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1984-3801 |
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http://hdl.handle.net/1843/43010 |
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eng |
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Global Science and Technology |
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Universidade Federal de Minas Gerais |
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UFMG |
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Brasil |
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ICA - INSTITUTO DE CIÊNCIAS AGRÁRIAS |
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Universidade Federal de Minas Gerais |
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