Machine learning models applied in the estimation of reference evapotranspiration from the Western Plateau of Paulista

Detalhes bibliográficos
Autor(a) principal: da Silva, Maurício Bruno Prado [UNESP]
Data de Publicação: 2022
Outros Autores: de Souza, Valter Cesar [UNESP], Cremasco, Caroline Pires [UNESP], Calça, Marcus Vinícius Contes [UNESP], Dos Santos, Cícero Manoel, Cremasco, Camila Pires [UNESP], Gabriel Filho, Luís Roberto Almeida [UNESP], Rodrigues, Sergio Augusto [UNESP], Escobedo, João Francisco [UNESP]
Tipo de documento: Artigo
Idioma: por
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.31413/nativa.v10i4.13922
http://hdl.handle.net/11449/249612
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 machine learning aplicados na estimação da evapotranspiração de referência do Planalto Ocidental Paulistaevapotranspirationmachine learningmodelingEvapotranspiration 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.Programa de Pós Graduação em Engenharia Agrícola Universidade Estadual Paulista, SPUniversidade Federal do Pará, PAPrograma de Pós Graduação em Engenharia Agrícola Universidade Estadual Paulista, SPUniversidade Estadual Paulista (UNESP)Universidade Federal do Pará (UFPA)da Silva, Maurício Bruno Prado [UNESP]de Souza, Valter Cesar [UNESP]Cremasco, Caroline Pires [UNESP]Calça, Marcus Vinícius Contes [UNESP]Dos Santos, Cícero ManoelCremasco, Camila Pires [UNESP]Gabriel Filho, Luís Roberto Almeida [UNESP]Rodrigues, Sergio Augusto [UNESP]Escobedo, João Francisco [UNESP]2023-07-29T16:04:27Z2023-07-29T16:04:27Z2022-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article506-515http://dx.doi.org/10.31413/nativa.v10i4.13922Nativa, v. 10, n. 4, p. 506-515, 2022.2318-7670http://hdl.handle.net/11449/24961210.31413/nativa.v10i4.139222-s2.0-85146999129Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPporNativainfo:eu-repo/semantics/openAccess2024-04-30T14:00:15Zoai:repositorio.unesp.br:11449/249612Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T14:01:25.504623Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)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 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
da Silva, Maurício Bruno Prado [UNESP]
evapotranspiration
machine learning
modeling
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 da Silva, Maurício Bruno Prado [UNESP]
author_facet da Silva, Maurício Bruno Prado [UNESP]
de Souza, Valter Cesar [UNESP]
Cremasco, Caroline Pires [UNESP]
Calça, Marcus Vinícius Contes [UNESP]
Dos Santos, Cícero Manoel
Cremasco, Camila Pires [UNESP]
Gabriel Filho, Luís Roberto Almeida [UNESP]
Rodrigues, Sergio Augusto [UNESP]
Escobedo, João Francisco [UNESP]
author_role author
author2 de Souza, Valter Cesar [UNESP]
Cremasco, Caroline Pires [UNESP]
Calça, Marcus Vinícius Contes [UNESP]
Dos Santos, Cícero Manoel
Cremasco, Camila Pires [UNESP]
Gabriel Filho, Luís Roberto Almeida [UNESP]
Rodrigues, Sergio Augusto [UNESP]
Escobedo, João Francisco [UNESP]
author2_role author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (UNESP)
Universidade Federal do Pará (UFPA)
dc.contributor.author.fl_str_mv da Silva, Maurício Bruno Prado [UNESP]
de Souza, Valter Cesar [UNESP]
Cremasco, Caroline Pires [UNESP]
Calça, Marcus Vinícius Contes [UNESP]
Dos Santos, Cícero Manoel
Cremasco, Camila Pires [UNESP]
Gabriel Filho, Luís Roberto Almeida [UNESP]
Rodrigues, Sergio Augusto [UNESP]
Escobedo, João Francisco [UNESP]
dc.subject.por.fl_str_mv evapotranspiration
machine learning
modeling
topic evapotranspiration
machine learning
modeling
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 2022-01-01
2023-07-29T16:04:27Z
2023-07-29T16:04:27Z
dc.type.status.fl_str_mv 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://dx.doi.org/10.31413/nativa.v10i4.13922
Nativa, v. 10, n. 4, p. 506-515, 2022.
2318-7670
http://hdl.handle.net/11449/249612
10.31413/nativa.v10i4.13922
2-s2.0-85146999129
url http://dx.doi.org/10.31413/nativa.v10i4.13922
http://hdl.handle.net/11449/249612
identifier_str_mv Nativa, v. 10, n. 4, p. 506-515, 2022.
2318-7670
10.31413/nativa.v10i4.13922
2-s2.0-85146999129
dc.language.iso.fl_str_mv por
language por
dc.relation.none.fl_str_mv Nativa
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 506-515
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
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