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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 original: |
Embrapa Cerrados (CPAC) |
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Biblioteca |
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Origem |
Tipo/Formato |
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Registro |
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Registro Completo
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Biblioteca(s): |
Embrapa Meio Ambiente. |
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Data corrente: |
19/11/2014 |
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Data da última atualização: |
19/11/2014 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 2 |
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Autoria: |
SÁ, A. L. B.; DIAS, A. C. F.; QUECINE, M. C.; COTTA, S. R.; FASANELLA, C. C.; ANDREOTE, F. D.; MELO, I. S. de. |
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Afiliação: |
ANDRE LUIS BRAGHINI SA; ARMANDO CAVALCANTE FRANCO DIAS, ESALQ-USP; MARIA CAROLINA QUECINE, ESALQ-USP; SIMONE RAPOSO COTTA, ESALQ-USP; CRISTIANE CIPOLLA FASANELLA, ESALQ-USP; FERNANDO DINI ANDREOTE, ESALQ-USP; ITAMAR SOARES DE MELO, CNPMA. |
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Título: |
Screening of endoglucanase-producing bacteria in the saline rhizosphere of Rhizophora mangle. |
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Ano de publicação: |
2014 |
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Fonte/Imprenta: |
Brazilian Journal of Microbiology, São Paulo, v. 45, n. 1, p. 193-197, 2014. |
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Idioma: |
Inglês |
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Conteúdo: |
Abstract: In screening the culturable endoglucanase-producing bacteria in the rhizosphere of Rhizophora mangle, we found a prevalence of genera Bacillus and Paenibacillus. These bacteria revealed different activities in endoglucolysis and biofilm formation when exposed to specific NaCl concentrations, indicating modulated growth under natural variations in mangrove salinity. |
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Palavras-Chave: |
Bacillus; Ecological behavior; Endoglucanase. |
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Thesagro: |
Bactéria; Mangue vermelho; Rhizophora mangle; Rizosfera. |
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Thesaurus NAL: |
Biofilm; Paenibacillus. |
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Categoria do assunto: |
S Ciências Biológicas |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/112000/1/2014-AP019.pdf
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Marc: |
LEADER 01244naa a2200301 a 4500
001 2000587
005 2014-11-19
008 2014 bl uuuu u00u1 u #d
100 1 $aSÁ, A. L. B.
245 $aScreening of endoglucanase-producing bacteria in the saline rhizosphere of Rhizophora mangle.$h[electronic resource]
260 $c2014
520 $aAbstract: In screening the culturable endoglucanase-producing bacteria in the rhizosphere of Rhizophora mangle, we found a prevalence of genera Bacillus and Paenibacillus. These bacteria revealed different activities in endoglucolysis and biofilm formation when exposed to specific NaCl concentrations, indicating modulated growth under natural variations in mangrove salinity.
650 $aBiofilm
650 $aPaenibacillus
650 $aBactéria
650 $aMangue vermelho
650 $aRhizophora mangle
650 $aRizosfera
653 $aBacillus
653 $aEcological behavior
653 $aEndoglucanase
700 1 $aDIAS, A. C. F.
700 1 $aQUECINE, M. C.
700 1 $aCOTTA, S. R.
700 1 $aFASANELLA, C. C.
700 1 $aANDREOTE, F. D.
700 1 $aMELO, I. S. de
773 $tBrazilian Journal of Microbiology, São Paulo$gv. 45, n. 1, p. 193-197, 2014.
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Embrapa Meio Ambiente (CNPMA) |
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