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Registro Completo |
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Biblioteca(s): |
Embrapa Algodão; Embrapa Arroz e Feijão; Embrapa Meio-Norte; Embrapa Milho e Sorgo. |
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Data corrente: |
03/12/2015 |
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Data da última atualização: |
28/03/2016 |
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Tipo da produção científica: |
Resumo em Anais de Congresso |
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Autoria: |
DENARDIN, J. E.; MUTADIUA, C. A. P.; MIRANDA, C. H. B.; SILVA, H. R. da; SILVA FILHO, P. M. da; FERREIRA, G. B.; CRUZ, I.; CARVALHO, M. da C. S.; ROCHA, M. de M.; NEUMAIER, N.; ALMEIDA, R. P. de; FAVARO, S. P.; SUALEI, F. J. |
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Afiliação: |
JOSE ELOIR DENARDIN, CNPT; CELSO AMÉRICO PEDRO MUTADIUA, MRE-ABC,IIAM-CZINw; CESAR HERACLIDES BEHLING MIRANDA, CNPAE; HENOQUE RIBEIRO DA SILVA, SRI; PEDRO MOREIRA DA SILVA FILHO, CNPSO; GILVAN BARBOSA FERREIRA, CNPA; IVAN CRUZ, CNPMS; MARIA DA CONCEICAO SANTANA CARVALHO, CNPAF; MAURISRAEL DE MOURA ROCHA, CPAMN; NORMAN NEUMAIER, CNPSO; RAUL PORFIRIO DE ALMEIDA, CNPA; SIMONE PALMA FAVARO, CNPAE; FERNANDO JOÃO SUALEI, IIAM-CZINw. |
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Título: |
Resposta da cultura de trigo à adubação com fósforo e potássio em Lichinga, Niassa, Moçambique. |
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Ano de publicação: |
2015 |
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Fonte/Imprenta: |
In: SEMINÁRIO DE DIVULGAÇÃO DE RESULTADOS DA INVESTIGAÇÃO AGRÁRIA NO CORREDOR DE NACALA, 2., 2015, Lichinga, Moçambique. Anais... Lichinga, Moçambique: Instituto de Investigação Agrária de Moçambique, 2015. |
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Páginas: |
p. 310-317. |
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Idioma: |
Português |
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Notas: |
Editores técnicos: Fernando João Sualei, Oscar Chichongue, Guilhermino Boina, Simone Palma Favaro, Cesar Heraclides Behling Miranda. |
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Thesagro: |
Aclimatação; Adubação; Fósforo; Potássio; Trigo; Variedade. |
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Categoria do assunto: |
F Plantas e Produtos de Origem Vegetal
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/131439/1/ID43289-2015SDRIACNacala-p310.pdf
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Marc: |
LEADER 01275naa a2200349 a 4500
001 2041864
005 2016-03-28
008 2015 bl uuuu u00u1 u #d
100 1 $aDENARDIN, J. E.
245 $aResposta da cultura de trigo à adubação com fósforo e potássio em Lichinga, Niassa, Moçambique.$h[electronic resource]
260 $c2015
300 $ap. 310-317.
500 $aEditores técnicos: Fernando João Sualei, Oscar Chichongue, Guilhermino Boina, Simone Palma Favaro, Cesar Heraclides Behling Miranda.
650 $aAclimatação
650 $aAdubação
650 $aFósforo
650 $aPotássio
650 $aTrigo
650 $aVariedade
700 1 $aMUTADIUA, C. A. P.
700 1 $aMIRANDA, C. H. B.
700 1 $aSILVA, H. R. da
700 1 $aSILVA FILHO, P. M. da
700 1 $aFERREIRA, G. B.
700 1 $aCRUZ, I.
700 1 $aCARVALHO, M. da C. S.
700 1 $aROCHA, M. de M.
700 1 $aNEUMAIER, N.
700 1 $aALMEIDA, R. P. de
700 1 $aFAVARO, S. P.
700 1 $aSUALEI, F. J.
773 $tIn: SEMINÁRIO DE DIVULGAÇÃO DE RESULTADOS DA INVESTIGAÇÃO AGRÁRIA NO CORREDOR DE NACALA, 2., 2015, Lichinga, Moçambique. Anais... Lichinga, Moçambique: Instituto de Investigação Agrária de Moçambique, 2015.
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Registro original: |
Embrapa Algodão (CNPA) |
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Registro |
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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. |
Registro Completo
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Biblioteca(s): |
Embrapa Cerrados. |
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Data corrente: |
19/08/2021 |
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Data da última atualização: |
19/08/2021 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
ALTHOFF, D.; RODRIGUES, L. N.; BAZAME, H. C. |
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Afiliação: |
DANIEL ALTHOFF; LINEU NEIVA RODRIGUES, CPAC; HELIZANI COUTO BAZAME. |
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Título: |
Uncertainty quantification for hydrological models based on neural networks: the dropout ensemble. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
Stochastic Environmental Research and Risk Assessment, v. 35, p. 1051?1067, 2021. |
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Páginas: |
p. 1051-1067 |
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DOI: |
https://doi.org/10.1007/s00477-021-01980-8 |
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Idioma: |
Inglês |
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Conteúdo: |
Abstract The use of neural networks in hydrology has been frequently undermined by limitations regarding the quantification of uncertainty in predictions. Many authors have proposed different methodologies to overcome these limitations, such as running Monte Carlo simulations, Bayesian approximations, and bootstrapping training samples, which come with computational limitations of their own, and two-step approaches, among others. One less-frequently explored alternative is to repurpose the dropout scheme during inference. Dropout is commonly used during training to avoid overfitting. However, it may also be activated during the testing period to effortlessly provide an ensemble of multiple ??sister?? predictions. This study explores the predictive uncertainty in hydrological models based on neural networks by comparing a multiparameter ensemble to a dropout ensemble. The dropout ensemble shows more reliable coverage of prediction intervals, while the multiparameter ensemble results in sharper prediction intervals. Moreover, for neural network structures with optimal lookback series, both ensemble strategies result in similar average interval scores. The dropout ensemble, however, benefits from requiring only a single calibration run, i.e., a single set of parameters. In addition, it delivers important insight for engineering design and decision-making with no increase in computational cost. Therefore, the dropout ensemble can be easily included in uncertainty analysis routines and even be combined with multiparameter or multimodel alternatives. MenosAbstract The use of neural networks in hydrology has been frequently undermined by limitations regarding the quantification of uncertainty in predictions. Many authors have proposed different methodologies to overcome these limitations, such as running Monte Carlo simulations, Bayesian approximations, and bootstrapping training samples, which come with computational limitations of their own, and two-step approaches, among others. One less-frequently explored alternative is to repurpose the dropout scheme during inference. Dropout is commonly used during training to avoid overfitting. However, it may also be activated during the testing period to effortlessly provide an ensemble of multiple ??sister?? predictions. This study explores the predictive uncertainty in hydrological models based on neural networks by comparing a multiparameter ensemble to a dropout ensemble. The dropout ensemble shows more reliable coverage of prediction intervals, while the multiparameter ensemble results in sharper prediction intervals. Moreover, for neural network structures with optimal lookback series, both ensemble strategies result in similar average interval scores. The dropout ensemble, however, benefits from requiring only a single calibration run, i.e., a single set of parameters. In addition, it delivers important insight for engineering design and decision-making with no increase in computational cost. Therefore, the dropout ensemble can be easily included in uncertainty analysis routin... Mostrar Tudo |
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Palavras-Chave: |
Modelo hidrológico; Rede neural. |
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Thesagro: |
Hidrologia. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02241naa a2200205 a 4500
001 2133739
005 2021-08-19
008 2021 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1007/s00477-021-01980-8$2DOI
100 1 $aALTHOFF, D.
245 $aUncertainty quantification for hydrological models based on neural networks$bthe dropout ensemble.$h[electronic resource]
260 $c2021
300 $ap. 1051-1067
520 $aAbstract The use of neural networks in hydrology has been frequently undermined by limitations regarding the quantification of uncertainty in predictions. Many authors have proposed different methodologies to overcome these limitations, such as running Monte Carlo simulations, Bayesian approximations, and bootstrapping training samples, which come with computational limitations of their own, and two-step approaches, among others. One less-frequently explored alternative is to repurpose the dropout scheme during inference. Dropout is commonly used during training to avoid overfitting. However, it may also be activated during the testing period to effortlessly provide an ensemble of multiple ??sister?? predictions. This study explores the predictive uncertainty in hydrological models based on neural networks by comparing a multiparameter ensemble to a dropout ensemble. The dropout ensemble shows more reliable coverage of prediction intervals, while the multiparameter ensemble results in sharper prediction intervals. Moreover, for neural network structures with optimal lookback series, both ensemble strategies result in similar average interval scores. The dropout ensemble, however, benefits from requiring only a single calibration run, i.e., a single set of parameters. In addition, it delivers important insight for engineering design and decision-making with no increase in computational cost. Therefore, the dropout ensemble can be easily included in uncertainty analysis routines and even be combined with multiparameter or multimodel alternatives.
650 $aHidrologia
653 $aModelo hidrológico
653 $aRede neural
700 1 $aRODRIGUES, L. N.
700 1 $aBAZAME, H. C.
773 $tStochastic Environmental Research and Risk Assessment$gv. 35, p. 1051?1067, 2021.
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