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Nonlinear hydrological time series modeling to forecast river level dynamics in the Rio Negro Uruguay basin.
Duque, Johan S; Santos, Rafael; Arteaga, Johny; Oyarzabal, Ricardo S; Santos, Leonardo B L.
Afiliação
  • Duque JS; National Institute for Space Research, INPE, São José dos Campos 12227-010, Brazil.
  • Santos R; Universidad Tecnológica del Uruguay, UTEC. ITR-CS, Durazno 97000, Uruguay.
  • Arteaga J; National Institute for Space Research, INPE, São José dos Campos 12227-010, Brazil.
  • Oyarzabal RS; USDA UV-B Monitoring and Research Program, Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, Colorado 80523, USA.
  • Santos LBL; National Center for Monitoring and Early Warning of Natural Disasters, Cemaden, São José dos Campos 12630-000, Brazil.
Chaos ; 34(5)2024 May 01.
Article em En | MEDLINE | ID: mdl-38780437
ABSTRACT
Floods significantly impact the well-being and development of communities. Hence, understanding their causes and establishing methodologies for risk prevention is a critical challenge for effective warning systems. Complex systems such as hydrological basins are modeled through hydrological models that have been utilized to understand water recharge of aquifers, available volume of dams, and floods in diverse regions. Acquiring real-time hydrometeorological data from basins and rivers is vital for establishing data-driven-based models as tools for the prediction of river-level dynamics and for understanding its nonlinear behavior. This paper introduces a hydrological model based on a multilayer perceptron neural network as a useful tool for time series modeling and forecasting river levels in three stations of the Rio Negro basin in Uruguay. Daily time series of river levels and rainfall serve as the input data for the model. The assessment of the models is based on metrics such as the Nash-Sutcliffe coefficient, the root mean square error, percent bias, and volumetric efficiency. The outputs exhibit varying model performance and accuracy during the prediction period across different sub-basin scales, revealing the neural network's ability to learn river dynamics. Lagged time series analysis demonstrates the potential for chaos in river-level time series over extended time periods, mainly when predicting dam-related scenarios, which shows physical connections between the dynamical system and the data-based model such as the evolution of the system over time.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE País/Região como assunto: America do sul / Uruguay Idioma: En Revista: Chaos Assunto da revista: CIENCIA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Brasil País de publicação: Estados Unidos
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE País/Região como assunto: America do sul / Uruguay Idioma: En Revista: Chaos Assunto da revista: CIENCIA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Brasil País de publicação: Estados Unidos