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| Registros recuperados : 17 | |
| 11. |  | GEROLD, G.; JUNGKUNST, H. F.; WANTZEN, K. M.; SCHÖNENBERG, R.; AMORIM, R. S. S.; COUTO, E. G.; MADARI, B.; HOHNWALD, S. (ed.). Interdisciplinary analysis carbon-optimized land management strategies for Southern Amazonia. Göttingen: Universitätsverlag Göttingen, 2014. 174 p. Carbocial Status Conference, 2013, Cuiabá.| Biblioteca(s): Embrapa Acre. |
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| 13. | | AIRES, U. R. V.; SILVA, D. D. da; FERNANDES FILHO, E. I.; RODRIGUES, L. N.; ULIANA, E. M.; AMORIM, R. S. S.; RIBEIRO, C. B. de M.; CAMPOS, J. A. Machine learning-based modeling of surface sediment concentration in Doce river basin. Journal of Hydrology, v. 619, 2023. e129320.| Biblioteca(s): Embrapa Cerrados. |
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| 14. | | GEROLD, G.; COUTO, E. G.; MADARI, B. E.; JUNGKUNST, H. F.; AMORIM, R. S. S.; HOHNWALD, S.; KLINGLER, M.; MACHADO, P. L. O. de A.; SCHÖNENBERG, R.; NENDEL, C. Carbon-optimised land management strategies for southern Amazonia. Regional Environmental Change, v. 18, n. 1, p. 1-9, 2018.| Biblioteca(s): Embrapa Arroz e Feijão. |
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| 15. | | SILVA, L. de C. M. da; PEIXOTO, D. S.; GOMES, J. B. V.; AVANZI, J. C.; AMORIM, R. S. S.; BORGHI, E.; RESENDE, A. V. de; SILVA, B. M.; MANCINI, M.; CURI, N. Mineralogy and pore size distribution of clayey Oxisols with granular structure and the effect of management systems. Soil & Tillage Research, v. 223, 105479, 2022.| Biblioteca(s): Embrapa Florestas; Embrapa Milho e Sorgo. |
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| 16. | | AIRES, U. R. V.; SILVA, D. D. da; FERNANDES FILHO, E. I.; RODRIGUES, L. N.; ULIANA, E. M.; AMORIM, R. S. S.; RIBEIRO, C. B. de M.; CAMPOS, J. A. Modeling of surface sediment concentration in the Doce River basin using satellite remote sensing. Journal of Environmental Management, v. 323, 2022. 15 p.| Biblioteca(s): Embrapa Cerrados. |
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| 17. | | MURGA-ORRILLO, H.; ABANTO-RODRIGUEZ, C.; DIONISIO, L. F. S.; CHU-KOO, F. W.; SCHWARTZ, G.; BUSTAMANTE, E. N.; STEWART, P. M.; AMORIM, R. S. S.; VOURLITIS, G. L.; LOBO, F. de A.; BARDALES-LOZANO, R. M. Tara (Caesalpinia spinosa) in natural and agroforestry systems under an altitudinal gradient in the Peruvian Andes: responses to soil and climate variation. Agronomy, v. 13, n. 2, Article 282, 2023.| Biblioteca(s): Embrapa Amazônia Oriental. |
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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: |
19/12/2023 |
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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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Circulação/Nível: |
A - 2 |
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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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