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

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dc.contributor.authorda Silva, Maurício Bruno Prado [UNESP]
dc.contributor.authorde Souza, Valter Cesar [UNESP]
dc.contributor.authorCremasco, Caroline Pires [UNESP]
dc.contributor.authorCalça, Marcus Vinícius Contes [UNESP]
dc.contributor.authorDos Santos, Cícero Manoel
dc.contributor.authorCremasco, Camila Pires [UNESP]
dc.contributor.authorGabriel Filho, Luís Roberto Almeida [UNESP]
dc.contributor.authorRodrigues, Sergio Augusto [UNESP]
dc.contributor.authorEscobedo, João Francisco [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.contributor.institutionUniversidade Federal do Pará (UFPA)
dc.date.accessioned2023-07-29T16:04:27Z
dc.date.available2023-07-29T16:04:27Z
dc.date.issued2022-01-01
dc.description.abstractEvapotranspiration 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.en
dc.description.affiliationPrograma de Pós Graduação em Engenharia Agrícola Universidade Estadual Paulista, SP
dc.description.affiliationUniversidade Federal do Pará, PA
dc.description.affiliationUnespPrograma de Pós Graduação em Engenharia Agrícola Universidade Estadual Paulista, SP
dc.format.extent506-515
dc.identifierhttp://dx.doi.org/10.31413/nativa.v10i4.13922
dc.identifier.citationNativa, v. 10, n. 4, p. 506-515, 2022.
dc.identifier.doi10.31413/nativa.v10i4.13922
dc.identifier.issn2318-7670
dc.identifier.scopus2-s2.0-85146999129
dc.identifier.urihttp://hdl.handle.net/11449/249612
dc.language.isopor
dc.relation.ispartofNativa
dc.sourceScopus
dc.subjectevapotranspiration
dc.subjectmachine learning
dc.subjectmodeling
dc.titleMachine learning models applied in the estimation of reference evapotranspiration from the Western Plateau of Paulistaen
dc.titleModelos de machine learning aplicados na estimação da evapotranspiração de referência do Planalto Ocidental Paulistapt
dc.typeArtigo
unesp.author.orcid0000-0001-5817-1409[1]
unesp.author.orcid0000-0001-5103-9771[2]
unesp.author.orcid0000-0002-9157-4653[3]
unesp.author.orcid0000-0002-5685-3980[4]
unesp.author.orcid0000-0002-6850-9757[5]
unesp.author.orcid0000-0003-2465-1361[6]
unesp.author.orcid0000-0002-7269-2806[7]
unesp.author.orcid0000-0002-2091-2141[8]
unesp.author.orcid0000-0002-8196-4447[9]
unesp.departmentEngenharia Rural - FCApt

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Botucatu - FCA - Faculdade de Ciências Agronômicas