Data/hora: 13/06/2024 09:06:25

Biblioteca(s): | Embrapa Cerrados.
Tipo da produção científica: | Artigo em Periódico Indexado
Circulação/Nível: | A - 2
Data corrente: | 19/12/2023
Data da última atualização: | 19/12/2023
Autoria: | GENEROSO, T. N.; SILVA, D. D. da; AMORIM, R. S. S.; RODRIGUES, L. N.; ALTHOFF, D.; SANTOS, E. P. dos.
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.
Título: | Forecasting of daily streamflows downstream from reservoirs with streamflow regularization using machine learning methods.
Ano de publicação: | 2023
Fonte/Imprenta: | Journal of South American Earth Sciences, v. 130, 104583, 2023.
DOI: | https://doi.org/10.1016/j.jsames.2023.104583
Idioma: | Inglês
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.
Palavras-Chave: | Filling missing data; Modeling; Reservoir outflow.
Categoria do assunto: | --
Registro original: | Embrapa Cerrados (CPAC)
Exemplares: | CPAC - (37679-1) - UPC - AP - DD - DIGITAL -  -  - DIS