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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: |
19/12/2023 |
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Data da última atualização: |
19/12/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
GENEROSO, T. N.; SILVA, D. D. da; AMORIM, R. S. S.; RODRIGUES, L. N.; ALTHOFF, D.; SANTOS, E. P. dos. |
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Afiliação: |
TARCILA NEVES GENEROSO, Universidade Federal de Viçosa; DEMETRIUS DAVID DA SILVA, Universidade Federal de Viçosa; RICARDO SANTOS SILVA AMORIM, Universidade Federal de Viçosa; LINEU NEIVA RODRIGUES, CPAC; DANIEL ALTHOFF, Universidade Federal de Viçosa; ERLI PINTO DOS SANTOS, Universidade Federal de Viçosa. |
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Título: |
Forecasting of daily streamflows downstream from reservoirs with streamflow regularization using machine learning methods. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
Journal of South American Earth Sciences, v. 130, 104583, 2023. |
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DOI: |
https://doi.org/10.1016/j.jsames.2023.104583 |
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Idioma: |
Inglês |
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Conteúdo: |
Streamflow gauge stations (SGS) can show inconsistent daily streamflow estimates, due to the number of readings taken by the rating-curve method, throughout the recording time series. For SGS located downstream of streamflow regularization reservoirs (SRR), the use of time series for the outflow can serve as a reference for improving these records, since the daily data are estimated by the water balance method, with about 24 daily flow records. This work aims to fit machine learning (ML) models to the forecasting of daily streamflow data of SGS located downstream of an SRR. Besides indicating inconsistencies in streamflow data from the SGS, the results also showed that, for the SGS close to the SRR, the model based on Neural Networks was the most accurate. For the SGS most distant from the SRR, the Multiple Linear Regression model was the best fit. |
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Palavras-Chave: |
Filling missing data; Modeling; Reservoir outflow. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 01616naa a2200229 a 4500
001 2159973
005 2023-12-19
008 2023 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1016/j.jsames.2023.104583$2DOI
100 1 $aGENEROSO, T. N.
245 $aForecasting of daily streamflows downstream from reservoirs with streamflow regularization using machine learning methods.$h[electronic resource]
260 $c2023
520 $aStreamflow gauge stations (SGS) can show inconsistent daily streamflow estimates, due to the number of readings taken by the rating-curve method, throughout the recording time series. For SGS located downstream of streamflow regularization reservoirs (SRR), the use of time series for the outflow can serve as a reference for improving these records, since the daily data are estimated by the water balance method, with about 24 daily flow records. This work aims to fit machine learning (ML) models to the forecasting of daily streamflow data of SGS located downstream of an SRR. Besides indicating inconsistencies in streamflow data from the SGS, the results also showed that, for the SGS close to the SRR, the model based on Neural Networks was the most accurate. For the SGS most distant from the SRR, the Multiple Linear Regression model was the best fit.
653 $aFilling missing data
653 $aModeling
653 $aReservoir outflow
700 1 $aSILVA, D. D. da
700 1 $aAMORIM, R. S. S.
700 1 $aRODRIGUES, L. N.
700 1 $aALTHOFF, D.
700 1 $aSANTOS, E. P. dos
773 $tJournal of South American Earth Sciences$gv. 130, 104583, 2023.
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Registro original: |
Embrapa Cerrados (CPAC) |
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Biblioteca |
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Origem |
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Cutter |
Registro |
Volume |
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URL |
Voltar
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Registro Completo
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Biblioteca(s): |
Embrapa Arroz e Feijão. |
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Data corrente: |
19/05/2014 |
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Data da última atualização: |
23/05/2014 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
B - 3 |
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Autoria: |
SIQUEIRA, B. dos S.; TEIXEIRA, J. V.; NÓBREGA, L. N. N.; BASSINELLO, P. Z. |
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Afiliação: |
BEATRIZ DOS SANTOS SIQUEIRA, bolsista CNPAF; JÉSSICA VIEIRA TEIXEIRA, bolsista CNPAF; LORRANA NARA NAVES NÓBREGA, bolsista CNPAF; PRISCILA ZACZUK BASSINELLO, CNPAF. |
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Título: |
Accelerated aging effects on the grain quality of stored carioca bean. |
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Ano de publicação: |
2014 |
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Fonte/Imprenta: |
Annual Report of the Bean Improvement Cooperative, Prosser, v. 57, p. 207-208, Mar. 2014. |
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ISSN: |
0084-7747 |
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Idioma: |
Inglês |
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Conteúdo: |
For research purposes, the accelerated aging of bean grains is one of the options available for the optimization of lab experiments in the evaluation of quality traits. Through an artificial aging simulator chamber of radiation it is possible to accelerate the aging process that occurs naturally, to predict and control the degree of darkening. This research evaluated the effect of the solar radiation simulator chamber on the technological characteristics of carioca bean grains, such as color and cooking time. |
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Thesagro: |
Armazenamento; Feijão; Grão; Phaseolus vulgaris. |
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Categoria do assunto: |
Q Alimentos e Nutrição Humana |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/102251/1/p206.pdf
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Marc: |
LEADER 01164naa a2200217 a 4500
001 1986437
005 2014-05-23
008 2014 bl uuuu u00u1 u #d
022 $a0084-7747
100 1 $aSIQUEIRA, B. dos S.
245 $aAccelerated aging effects on the grain quality of stored carioca bean.$h[electronic resource]
260 $c2014
520 $aFor research purposes, the accelerated aging of bean grains is one of the options available for the optimization of lab experiments in the evaluation of quality traits. Through an artificial aging simulator chamber of radiation it is possible to accelerate the aging process that occurs naturally, to predict and control the degree of darkening. This research evaluated the effect of the solar radiation simulator chamber on the technological characteristics of carioca bean grains, such as color and cooking time.
650 $aArmazenamento
650 $aFeijão
650 $aGrão
650 $aPhaseolus vulgaris
700 1 $aTEIXEIRA, J. V.
700 1 $aNÓBREGA, L. N. N.
700 1 $aBASSINELLO, P. Z.
773 $tAnnual Report of the Bean Improvement Cooperative, Prosser$gv. 57, p. 207-208, Mar. 2014.
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Embrapa Arroz e Feijão (CNPAF) |
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