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Biblioteca(s): |
Embrapa Semiárido. |
Data corrente: |
25/07/2017 |
Data da última atualização: |
16/04/2024 |
Tipo da produção científica: |
Artigo em Anais de Congresso |
Autoria: |
CARNEIRO NETO, T. F. de S.; XAVIER, C. F.; SANTOS, M. V. L.; SILVA, R. C. S. da; KIILL, L. H. P. |
Afiliação: |
THIAGO FRANCISCO DE SOUZA CARNEIRO NETO, UNIVERSIDADE DO ESTADO DA BAHIA; CARINE FEITOSA XAVIER, UNIVERSIDADE DO ESTADO DA BAHIA; MARCUS VINICIUS LEITE SANTOS, UNIVERSIDADE DO ESTADO DA BAHIA; RAÍRA CARINE SANTANA DA SILVA, UNIVERSIDADE DE PERNAMBUCO; LUCIA HELENA PIEDADE KIILL, CPATSA. |
Título: |
Viabilidade polínica e receptividade estigmática de duas variedades de aceroleira. |
Ano de publicação: |
2017 |
Fonte/Imprenta: |
In: JORNADA DE INICIAÇÃO CIENTÍFICA DA EMBRAPA SEMIÁRIDO, 12., 2017, Petrolina. Anais... Petrolina: Embrapa Semiárido, 2017. |
Páginas: |
p. 27-32. |
Série: |
(Embrapa Semiárido. Documentos, 279). |
Idioma: |
Português |
Conteúdo: |
O objetivo deste trabalho foi avaliar o percentual de viabilidade polínica e receptividade estigmática de duas variedades de aceroleira em cultivo irrigado, em diferentes horários ao longo da manhã. |
Palavras-Chave: |
Cereja das Antilhas; Grãos de pólen. |
Thesagro: |
Acerola; Fruta tropical; Polinização. |
Thesaurus Nal: |
Malpighia emarginata; Malpighiaceae. |
Categoria do assunto: |
F Plantas e Produtos de Origem Vegetal |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/162038/1/Artigo-3.pdf
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Marc: |
LEADER 01085nam a2200265 a 4500 001 2073135 005 2024-04-16 008 2017 bl uuuu u00u1 u #d 100 1 $aCARNEIRO NETO, T. F. de S. 245 $aViabilidade polínica e receptividade estigmática de duas variedades de aceroleira.$h[electronic resource] 260 $aIn: JORNADA DE INICIAÇÃO CIENTÍFICA DA EMBRAPA SEMIÁRIDO, 12., 2017, Petrolina. Anais... Petrolina: Embrapa Semiárido$c2017 300 $ap. 27-32. 490 $a(Embrapa Semiárido. Documentos, 279). 520 $aO objetivo deste trabalho foi avaliar o percentual de viabilidade polínica e receptividade estigmática de duas variedades de aceroleira em cultivo irrigado, em diferentes horários ao longo da manhã. 650 $aMalpighia emarginata 650 $aMalpighiaceae 650 $aAcerola 650 $aFruta tropical 650 $aPolinização 653 $aCereja das Antilhas 653 $aGrãos de pólen 700 1 $aXAVIER, C. F. 700 1 $aSANTOS, M. V. L. 700 1 $aSILVA, R. C. S. da 700 1 $aKIILL, L. H. P.
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Embrapa Semiárido (CPATSA) |
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Registro Completo
Biblioteca(s): |
Embrapa Cerrados. |
Data corrente: |
19/08/2021 |
Data da última atualização: |
19/08/2021 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 1 |
Autoria: |
ALTHOFF, D.; RODRIGUES, L. N.; BAZAME, H. C. |
Afiliação: |
DANIEL ALTHOFF; LINEU NEIVA RODRIGUES, CPAC; HELIZANI COUTO BAZAME. |
Título: |
Uncertainty quantification for hydrological models based on neural networks: the dropout ensemble. |
Ano de publicação: |
2021 |
Fonte/Imprenta: |
Stochastic Environmental Research and Risk Assessment, v. 35, p. 1051?1067, 2021. |
Páginas: |
p. 1051-1067 |
DOI: |
https://doi.org/10.1007/s00477-021-01980-8 |
Idioma: |
Inglês |
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 |
Palavras-Chave: |
Modelo hidrológico; Rede neural. |
Thesagro: |
Hidrologia. |
Categoria do assunto: |
-- |
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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