MODELOS DE MACHINE LEARNING APLICADOS NA ESTIMAÇÃO DA EVAPOTRANSPIRAÇÃO DE REFERÊNCIA DO PLANALTO OCIDENTAL PAULISTA

Autores

  • Maurício Bruno Prado da Silva mauricio.prado19@hotmail.com
    Programa de Pós Graduação em Engenharia Agrícola, Universidade Estadual Paulista, Botucatu, SP, Brasil. https://orcid.org/0000-0001-5817-1409
  • Valter Cesar de Souza valter.souza@unesp.br
    Programa de Pós Graduação em Engenharia Agrícola, Universidade Estadual Paulista, Botucatu, SP, Brasil. https://orcid.org/0000-0001-5103-9771
  • Caroline Pires Cremasco caroline.cremasco@unesp.br
    Unesp - Universidade Estadual Paulista
  • Marcus Vinícius Contes Calça mcontes@outlook.com
    Programa de Pós Graduação em Engenharia Agrícola, Universidade Estadual Paulista, Botucatu, SP, Brasil. https://orcid.org/0000-0002-5685-3980
  • Cícero Manoel dos Santos ciceromanoel2007@gmail.com
    Universidade Federal do Pará, Belém, PA, Brasil. https://orcid.org/0000-0002-6850-9757
  • Camila Pires Cremasco camila.cremasco@unesp.br
    Programa de Pós Graduação em Engenharia Agrícola, Universidade Estadual Paulista, Botucatu, SP, Brasil. https://orcid.org/0000-0003-2465-1361
  • Luís Roberto Almeida Gabriel Filho gabriel.filho@unesp.br
    Programa de Pós Graduação em Engenharia Agrícola, Universidade Estadual Paulista, Botucatu, SP, Brasil. https://orcid.org/0000-0002-7269-2806
  • Sergio Augusto Rodrigues sergio.rodrigues@unesp.br
    Programa de Pós Graduação em Engenharia Agrícola, Universidade Estadual Paulista, Botucatu, SP, Brasil. https://orcid.org/0000-0002-2091-2141
  • João Francisco Escobedo j.escobedo@unesp.br
    Programa de Pós Graduação em Engenharia Agrícola, Universidade Estadual Paulista, Botucatu, SP, Brasil. https://orcid.org/0000-0002-8196-4447

DOI:

10.31413/nativa.v10i4.13922

Palavras-chave:

evapotranspiração, machine learning, modelagem da evapotranspiração de referência, aprendizagem de máquina.

Resumo

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.

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2022-11-16 — Atualizado em 2024-06-11

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Silva, M. B. P. da, Souza, V. C. de, Pires Cremasco, C., Calça, M. V. C., Santos, C. M. dos, Cremasco, C. P., Gabriel Filho, L. R. A., Rodrigues, S. A., & Escobedo, J. F. (2024). MODELOS DE MACHINE LEARNING APLICADOS NA ESTIMAÇÃO DA EVAPOTRANSPIRAÇÃO DE REFERÊNCIA DO PLANALTO OCIDENTAL PAULISTA. Nativa, 10(4), 506–515. https://doi.org/10.31413/nativa.v10i4.13922 (Original work published 16º de novembro de 2022)

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Engenharia Agrícola / Agricultural Engineering

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