MACHINE LEARNING MODELS APPLIED IN THE ESTIMATION OF REFERENCE EVAPOTRANSPIRATION FROM THE WESTERN PLATEAU OF PAULISTA

Detalhes bibliográficos
Autor(a) principal: Silva, Maurício Bruno Prado da
Data de Publicação: 2022
Outros Autores: Souza, Valter Cesar de, Pires Cremasco, Caroline, Calça, Marcus Vinícius Contes, Santos, Cícero Manoel dos, Cremasco, Camila Pires, Gabriel Filho, Luís Roberto Almeida, Rodrigues, Sergio Augusto, Escobedo, João Francisco
Tipo de documento: Artigo
Idioma: por
Título da fonte: Nativa (Sinop)
Texto Completo: https://periodicoscientificos.ufmt.br/ojs/index.php/nativa/article/view/13922
Resumo: Evapotranspiration depends on the interaction between meteorological variables (solar radiation, air temperature, precipitation, relative humidity and wind speed) and phytosanitary conditions of agricultural crops. It is complex to build reliable evapotranspiration measurements due to the high costs of implementing micrometeorological techniques, in addition to difficulties in the operation and maintenance of the necessary equipment. The purpose of this research was to model the reference evapotranspiration through machine learning techniques in climatic data from 30 automatic weather stations in the Planalto Ocidental Paulista, State of São Paulo, Brazil, in the period 2013-2017. A comparison of the statistical performance between the techniques used was carried out, where the best performance of the EToMLP4 model (rRMSE = 0.62%), followed by EToANFIS4 (rRMSE = 0.75%), EToSVM4 (rRMSE = 1.19%) and EToGRNN4 (rRMSE = 11.05 %). Performance measures of the validation base show that the proposed models are able to estimate the reference evapotranspiration, with emphasis on the MPL technique.
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spelling MACHINE LEARNING MODELS APPLIED IN THE ESTIMATION OF REFERENCE EVAPOTRANSPIRATION FROM THE WESTERN PLATEAU OF PAULISTAMODELOS DE APRENDIZAJE DE MÁQUINAS APLICADOS EN LA ESTIMACIÓN DE LA EVAPOTRANSPIRACIÓN DE REFERENCIA DE LA MESETA OCCIDENTAL DE PAULISTAMODELOS DE MACHINE LEARNING APLICADOS NA ESTIMAÇÃO DA EVAPOTRANSPIRAÇÃO DE REFERÊNCIA DO PLANALTO OCIDENTAL PAULISTAevapotranspiraciónaprendizaje automáticomodelado de evapotranspiración de referenciaaprendizaje automático.evapotranspirationmachine learningreference evapotranspiration modelingmachine learning.evapotranspiraçãomachine learningmodelagem da evapotranspiração de referênciaaprendizagem de máquina.Evapotranspiration depends on the interaction between meteorological variables (solar radiation, air temperature, precipitation, relative humidity and wind speed) and phytosanitary conditions of agricultural crops. It is complex to build reliable evapotranspiration measurements due to the high costs of implementing micrometeorological techniques, in addition to difficulties in the operation and maintenance of the necessary equipment. The purpose of this research was to model the reference evapotranspiration through machine learning techniques in climatic data from 30 automatic weather stations in the Planalto Ocidental Paulista, State of São Paulo, Brazil, in the period 2013-2017. A comparison of the statistical performance between the techniques used was carried out, where the best performance of the EToMLP4 model (rRMSE = 0.62%), followed by EToANFIS4 (rRMSE = 0.75%), EToSVM4 (rRMSE = 1.19%) and EToGRNN4 (rRMSE = 11.05 %). Performance measures of the validation base show that the proposed models are able to estimate the reference evapotranspiration, with emphasis on the MPL technique.La evapotranspiración depende de la interacción entre las variables meteorológicas (radiación solar, temperatura del aire, precipitación, humedad relativa y velocidad del viento) y las condiciones fitosanitarias de los cultivos agrícolas. Es complejo construir mediciones confiables de evapotranspiración debido a los altos costos de implementar técnicas micrometeorológicas, además de las dificultades en la operación y mantenimiento de los equipos necesarios. El objetivo de esta investigación fue modelar la evapotranspiración de referencia a través de técnicas de aprendizaje automático en datos climáticos de 30 estaciones meteorológicas automáticas en el Planalto Ocidental Paulista, Estado de São Paulo, Brasil, en el período 2013-2017. Se realizó una comparación del rendimiento estadístico entre las técnicas utilizadas, donde el mejor rendimiento del modelo EToMLP4 (rRMSE = 0,62%), seguido de EToANFIS4 (rRMSE = 0,75%), EToSVM4 (rRMSE = 1,19%) y EToGRNN4 (rRMSE = 11,05 %). Las medidas de desempeño de la base de validación muestran que los modelos propuestos son capaces de estimar la evapotranspiración de referencia, con énfasis en la técnica MPL.A evapotranspiração depende da interação entre variáveis meteorológicas (radiação solar, temperatura do ar, precipitação, umidade relativa do ar e velocidade do vento) e condições fitossanitárias das culturas agrícolas. É complexo construir medidas confiáveis de evapotranspiração devido aos elevados custos para implantação de técnicas micrometeorológicas, além de dificuldades na operação e manutenção dos equipamentos necessários. O propósito desta pesquisa foi modelar a evapotranspiração de referência (ETo) por meio de técnicas de machine learning em dados climáticos de 30 estações meteorológicas automáticas do Planalto Ocidental Paulista, Estado de São Paulo, Brasil, no período de 2013-2017. Uma comparação do desempenho estatístico entre as técnicas utilizadas foi realizada onde constatou-se melhor desempenho do modelo EToMLP4 (rRMSE = 0.62%), seguido por EToANFIS4 (rRMSE = 0.75%), EToSVM4 (rRMSE = 1.19%) e EToGRNN4 (rRMSE = 11.05%). Medidas de performance da base de validação evidenciam que os modelos propostos são aptos à estimativa da evapotranspiração de referência com destaque para a técnica MPL. Palavras-chave: evapotranspiração; modelagem matemática; aprendizagem de máquina.   Machine learning models applied in the estimation of reference evapotranspiration from the Western Plateau of Paulista   ABSTRACT: Evapotranspiration depends on the interaction between meteorological variables (solar radiation, air temperature, precipitation, relative humidity and wind speed) and phytosanitary conditions of agricultural crops. It is complex to build reliable evapotranspiration measurements due to the high costs of implementing micrometeorological techniques, in addition to difficulties in the operation and maintenance of the necessary equipment. The purpose of this research was to model the reference evapotranspiration through machine learning techniques in climatic data from 30 automatic weather stations in the Planalto Ocidental Paulista, State of São Paulo, Brazil, in the period 2013-2017. A comparison of the statistical performance between the techniques used was carried out, where the best performance of the EToMLP4 model (rRMSE = 0.62%), followed by EToANFIS4 (rRMSE = 0.75%), EToSVM4 (rRMSE = 1.19%) and EToGRNN4 (rRMSE = 11.05 %). Performance measures of the validation base show that the proposed models are able to estimate the reference evapotranspiration, with emphasis on the MPL technique. Keywords: evapotranspiration; modeling; machine learning.Universidade Federal de Mato Grosso2022-11-16info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://periodicoscientificos.ufmt.br/ojs/index.php/nativa/article/view/1392210.31413/nativa.v10i4.13922Nativa; v. 10 n. 4 (2022); 506-515Nativa; Vol. 10 Núm. 4 (2022); 506-515Nativa; Vol. 10 No. 4 (2022); 506-5152318-7670reponame:Nativa (Sinop)instname:Universidade Federal de Mato Grosso (UFMT)instacron:UFMTporhttps://periodicoscientificos.ufmt.br/ojs/index.php/nativa/article/view/13922/11625Copyright (c) 2022 Nativahttps://creativecommons.org/licenses/by-nc/4.0info:eu-repo/semantics/openAccessSilva, Maurício Bruno Prado daSouza, Valter Cesar dePires Cremasco, CarolineCalça, Marcus Vinícius ContesSantos, Cícero Manoel dosCremasco, Camila PiresGabriel Filho, Luís Roberto AlmeidaRodrigues, Sergio AugustoEscobedo, João Francisco2022-11-02T00:23:39Zoai:periodicoscientificos.ufmt.br:article/13922Revistahttps://periodicoscientificos.ufmt.br/ojs/index.php/nativaPUBhttps://periodicoscientificos.ufmt.br/ojs/index.php/nativa/oai||rrmelo2@yahoo.com.br2318-76702318-7670opendoar:2022-11-02T00:23:39Nativa (Sinop) - Universidade Federal de Mato Grosso (UFMT)false
dc.title.none.fl_str_mv MACHINE LEARNING MODELS APPLIED IN THE ESTIMATION OF REFERENCE EVAPOTRANSPIRATION FROM THE WESTERN PLATEAU OF PAULISTA
MODELOS DE APRENDIZAJE DE MÁQUINAS APLICADOS EN LA ESTIMACIÓN DE LA EVAPOTRANSPIRACIÓN DE REFERENCIA DE LA MESETA OCCIDENTAL DE PAULISTA
MODELOS DE MACHINE LEARNING APLICADOS NA ESTIMAÇÃO DA EVAPOTRANSPIRAÇÃO DE REFERÊNCIA DO PLANALTO OCIDENTAL PAULISTA
title MACHINE LEARNING MODELS APPLIED IN THE ESTIMATION OF REFERENCE EVAPOTRANSPIRATION FROM THE WESTERN PLATEAU OF PAULISTA
spellingShingle MACHINE LEARNING MODELS APPLIED IN THE ESTIMATION OF REFERENCE EVAPOTRANSPIRATION FROM THE WESTERN PLATEAU OF PAULISTA
Silva, Maurício Bruno Prado da
evapotranspiración
aprendizaje automático
modelado de evapotranspiración de referencia
aprendizaje automático.
evapotranspiration
machine learning
reference evapotranspiration modeling
machine learning.
evapotranspiração
machine learning
modelagem da evapotranspiração de referência
aprendizagem de máquina.
title_short MACHINE LEARNING MODELS APPLIED IN THE ESTIMATION OF REFERENCE EVAPOTRANSPIRATION FROM THE WESTERN PLATEAU OF PAULISTA
title_full MACHINE LEARNING MODELS APPLIED IN THE ESTIMATION OF REFERENCE EVAPOTRANSPIRATION FROM THE WESTERN PLATEAU OF PAULISTA
title_fullStr MACHINE LEARNING MODELS APPLIED IN THE ESTIMATION OF REFERENCE EVAPOTRANSPIRATION FROM THE WESTERN PLATEAU OF PAULISTA
title_full_unstemmed MACHINE LEARNING MODELS APPLIED IN THE ESTIMATION OF REFERENCE EVAPOTRANSPIRATION FROM THE WESTERN PLATEAU OF PAULISTA
title_sort MACHINE LEARNING MODELS APPLIED IN THE ESTIMATION OF REFERENCE EVAPOTRANSPIRATION FROM THE WESTERN PLATEAU OF PAULISTA
author Silva, Maurício Bruno Prado da
author_facet Silva, Maurício Bruno Prado da
Souza, Valter Cesar de
Pires Cremasco, Caroline
Calça, Marcus Vinícius Contes
Santos, Cícero Manoel dos
Cremasco, Camila Pires
Gabriel Filho, Luís Roberto Almeida
Rodrigues, Sergio Augusto
Escobedo, João Francisco
author_role author
author2 Souza, Valter Cesar de
Pires Cremasco, Caroline
Calça, Marcus Vinícius Contes
Santos, Cícero Manoel dos
Cremasco, Camila Pires
Gabriel Filho, Luís Roberto Almeida
Rodrigues, Sergio Augusto
Escobedo, João Francisco
author2_role author
author
author
author
author
author
author
author
dc.contributor.author.fl_str_mv Silva, Maurício Bruno Prado da
Souza, Valter Cesar de
Pires Cremasco, Caroline
Calça, Marcus Vinícius Contes
Santos, Cícero Manoel dos
Cremasco, Camila Pires
Gabriel Filho, Luís Roberto Almeida
Rodrigues, Sergio Augusto
Escobedo, João Francisco
dc.subject.por.fl_str_mv evapotranspiración
aprendizaje automático
modelado de evapotranspiración de referencia
aprendizaje automático.
evapotranspiration
machine learning
reference evapotranspiration modeling
machine learning.
evapotranspiração
machine learning
modelagem da evapotranspiração de referência
aprendizagem de máquina.
topic evapotranspiración
aprendizaje automático
modelado de evapotranspiración de referencia
aprendizaje automático.
evapotranspiration
machine learning
reference evapotranspiration modeling
machine learning.
evapotranspiração
machine learning
modelagem da evapotranspiração de referência
aprendizagem de máquina.
description Evapotranspiration depends on the interaction between meteorological variables (solar radiation, air temperature, precipitation, relative humidity and wind speed) and phytosanitary conditions of agricultural crops. It is complex to build reliable evapotranspiration measurements due to the high costs of implementing micrometeorological techniques, in addition to difficulties in the operation and maintenance of the necessary equipment. The purpose of this research was to model the reference evapotranspiration through machine learning techniques in climatic data from 30 automatic weather stations in the Planalto Ocidental Paulista, State of São Paulo, Brazil, in the period 2013-2017. A comparison of the statistical performance between the techniques used was carried out, where the best performance of the EToMLP4 model (rRMSE = 0.62%), followed by EToANFIS4 (rRMSE = 0.75%), EToSVM4 (rRMSE = 1.19%) and EToGRNN4 (rRMSE = 11.05 %). Performance measures of the validation base show that the proposed models are able to estimate the reference evapotranspiration, with emphasis on the MPL technique.
publishDate 2022
dc.date.none.fl_str_mv 2022-11-16
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://periodicoscientificos.ufmt.br/ojs/index.php/nativa/article/view/13922
10.31413/nativa.v10i4.13922
url https://periodicoscientificos.ufmt.br/ojs/index.php/nativa/article/view/13922
identifier_str_mv 10.31413/nativa.v10i4.13922
dc.language.iso.fl_str_mv por
language por
dc.relation.none.fl_str_mv https://periodicoscientificos.ufmt.br/ojs/index.php/nativa/article/view/13922/11625
dc.rights.driver.fl_str_mv Copyright (c) 2022 Nativa
https://creativecommons.org/licenses/by-nc/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2022 Nativa
https://creativecommons.org/licenses/by-nc/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidade Federal de Mato Grosso
publisher.none.fl_str_mv Universidade Federal de Mato Grosso
dc.source.none.fl_str_mv Nativa; v. 10 n. 4 (2022); 506-515
Nativa; Vol. 10 Núm. 4 (2022); 506-515
Nativa; Vol. 10 No. 4 (2022); 506-515
2318-7670
reponame:Nativa (Sinop)
instname:Universidade Federal de Mato Grosso (UFMT)
instacron:UFMT
instname_str Universidade Federal de Mato Grosso (UFMT)
instacron_str UFMT
institution UFMT
reponame_str Nativa (Sinop)
collection Nativa (Sinop)
repository.name.fl_str_mv Nativa (Sinop) - Universidade Federal de Mato Grosso (UFMT)
repository.mail.fl_str_mv ||rrmelo2@yahoo.com.br
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