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 | Acesso ao texto completo restrito à biblioteca da Embrapa Cerrados. Para informações adicionais entre em contato com cpac.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Cerrados. |
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Data corrente: |
03/12/2020 |
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Data da última atualização: |
07/12/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
FARIAS, D. B. dos; ALTHOFF, D.; RODRIGUES, L. N.; FILGUEIRAS, R. |
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Afiliação: |
LINEU NEIVA RODRIGUES, CPAC. |
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Título: |
Performance evaluation of numerical and machine learning methods in estimating reference evapotranspiration in a Brazilian agricultural frontier. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Theoretical and Applied Climatology, 2020. |
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Páginas: |
12 p. |
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DOI: |
https://doi.org/10.1007/s00704-020-03380-4 |
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Idioma: |
Português |
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Conteúdo: |
The reference evapotranspiration (ET0) estimates is important for water resources and irrigation management. The Penman- Monteith equation is known for its accuracy but requires a high number of climatic parameters that are not always available. Thus, this study aimed to evaluate the performance of machine learning techniques (cubist regression, artificial neural network with Bayesian regularization, support vector machine with linear kernel function) and stepwisemultiple linear regressionmethod to estimate daily ET0 with limited weather data in a Brazilian agricultural frontier (MATOPIBA). Climatic data from 2000 to 2016 obtained from 23 weather stations were used. Five data scenarios were evaluated: (i) all variables, (ii) radiation and temperature, (iii) temperature and relative humidity, (iv) wind speed and temperature, and (v) temperature. The results showed that the machine learning methods are robust in estimating ET0, even in the absence of some variables. Among the methods evaluated using only temperature data, the cubist regression showed better performance. When estimating water demand for soybean and maize crops using only temperature, the cubist regression and calibrated Hargreaves-Samani equation showed the smallest errors. |
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Thesagro: |
Evapotranspiração; Modelo Matemático. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 01944naa a2200205 a 4500
001 2127569
005 2020-12-07
008 2020 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1007/s00704-020-03380-4$2DOI
100 1 $aFARIAS, D. B. dos
245 $aPerformance evaluation of numerical and machine learning methods in estimating reference evapotranspiration in a Brazilian agricultural frontier.$h[electronic resource]
260 $c2020
300 $a12 p.
520 $aThe reference evapotranspiration (ET0) estimates is important for water resources and irrigation management. The Penman- Monteith equation is known for its accuracy but requires a high number of climatic parameters that are not always available. Thus, this study aimed to evaluate the performance of machine learning techniques (cubist regression, artificial neural network with Bayesian regularization, support vector machine with linear kernel function) and stepwisemultiple linear regressionmethod to estimate daily ET0 with limited weather data in a Brazilian agricultural frontier (MATOPIBA). Climatic data from 2000 to 2016 obtained from 23 weather stations were used. Five data scenarios were evaluated: (i) all variables, (ii) radiation and temperature, (iii) temperature and relative humidity, (iv) wind speed and temperature, and (v) temperature. The results showed that the machine learning methods are robust in estimating ET0, even in the absence of some variables. Among the methods evaluated using only temperature data, the cubist regression showed better performance. When estimating water demand for soybean and maize crops using only temperature, the cubist regression and calibrated Hargreaves-Samani equation showed the smallest errors.
650 $aEvapotranspiração
650 $aModelo Matemático
700 1 $aALTHOFF, D.
700 1 $aRODRIGUES, L. N.
700 1 $aFILGUEIRAS, R.
773 $tTheoretical and Applied Climatology, 2020.
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Registro Completo
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Biblioteca(s): |
Embrapa Agroindústria de Alimentos. |
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Data corrente: |
01/03/2021 |
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Data da última atualização: |
02/03/2021 |
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Tipo da produção científica: |
Capítulo em Livro Técnico-Científico |
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Autoria: |
AMARAL, W. DO; DESCHAMPS, C.; BIZZO, H. R.; PINTO, M. A. S.; SILVA, L. E. DA; BIASI, L. A.; FREITAS, T. S.; ROCHA, J. E.; CAMPINA, F. F.; COSTA, M. S.; BEZERRA, C. F.; COUTINHO, H. D. M. |
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Afiliação: |
Wanderlei do Amaral, Universidade Federal do Paraná; Cícero Deschamps, UFPR; HUMBERTO RIBEIRO BIZZO, CTAA; Marco Antonio S. Pinto, UFPR; Luiz Everson da Silva, UFPR; Luiz A. Biasi, UFPR; Thiago S. Freitas, UFPR; Janaína E. Rocha, UFPR; Fábia F. Campina, UFPR; Maria S. Costa, UFPR; Camila F. Bezerra, UFPR; Henrique D. M. Coutinho, UFPR. |
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Título: |
Essential Oil of Baccharis milleflora in the Atlantic Rain Forest of the Paraná State in Brazil: Chemical Composition and Biological Evaluation. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
In: AKHTAR, M. S.; SWAMY, M. K.; SINNIAH, U. R. (Ed.). Natural Bio-active Compounds: Production and Applications. Singapore : Springer Nature Singapore Pte Ltd., 2019. Cap. 22. |
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Volume: |
v. 1 |
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Páginas: |
p. 599-608 |
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Idioma: |
Inglês |
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Palavras-Chave: |
Atlantic Forest; Baccharis milleflora; Essential oil; Terpenes. |
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Thesagro: |
Carqueja. |
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Thesaurus NAL: |
Medicinal plants. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 01124naa a2200337 a 4500
001 2130391
005 2021-03-02
008 2019 bl uuuu u00u1 u #d
100 1 $aAMARAL, W. DO
245 $aEssential Oil of Baccharis milleflora in the Atlantic Rain Forest of the Paraná State in Brazil$bChemical Composition and Biological Evaluation.$h[electronic resource]
260 $c2019
300 $ap. 599-608 v. 1
490 $vv. 1
650 $aMedicinal plants
650 $aCarqueja
653 $aAtlantic Forest
653 $aBaccharis milleflora
653 $aEssential oil
653 $aTerpenes
700 1 $aDESCHAMPS, C.
700 1 $aBIZZO, H. R.
700 1 $aPINTO, M. A. S.
700 1 $aSILVA, L. E. DA
700 1 $aBIASI, L. A.
700 1 $aFREITAS, T. S.
700 1 $aROCHA, J. E.
700 1 $aCAMPINA, F. F.
700 1 $aCOSTA, M. S.
700 1 $aBEZERRA, C. F.
700 1 $aCOUTINHO, H. D. M.
773 $tIn: AKHTAR, M. S.; SWAMY, M. K.; SINNIAH, U. R. (Ed.). Natural Bio-active Compounds: Production and Applications. Singapore : Springer Nature Singapore Pte Ltd., 2019. Cap. 22.
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Embrapa Agroindústria de Alimentos (CTAA) |
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