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A Multi-model Framework for Streamflow Forecasting Based on Stochastic Models: an Application to the State Of Ceará, Brazil

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Abstract

Reliable long-term (decadal scale) streamflow prediction would provide significant planning information for water resources management, particularly in areas marked by significant variability at those time scales. In this study, a multi-model for prediction using four models that incorporate preprocessing methods along with data-driven forecast models coupled using the least absolute shrinkage and selection operator (LASSO) regression method is proposed. Models utilized complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and wavelet transform (WT) as the decomposition methods and autoregressive (AR) and hidden Markov models (HMM) as the predictive method. The model is evaluated in a comparative analysis with a variety of models previously proposed for hydrological time series prediction. We compare the predictive skill of alternative data-driven models for average annual streamflow (3 ~ 15 years) prediction. Results indicate that the multi-model performed better than the other models, presenting lower values of MAE and RSME. This multi-model can be a reliable tool for forecasting, which can be explored for hydrological data that have remarkably nonlinear and nonstationary features.

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Data Availability

Data was retrieved from the Brazilian National Water Agency (ANA) at http://www.snirh.gov.br/hidroweb/.

Code Availability

The calculations and figures were made using the R software.

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Acknowledgements

This study was financed in part by the Conselho Nacional de Desenvolvimento Científico e Tecnológico—Brasil (CNPq), the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES), and the Fundação Cearense de Apoio ao Desenvolvimento Científico e Tecnológico (FUNCAP).

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Authors and Affiliations

  1. Dept. of Hydraulic and Environmental Engineering, Federal University of Ceará, Fortaleza, Ceará, 60455-900, Brazil

    Larissa Zaira Rafael Rolim & Francisco de Assis de Souza Filho

  2. Department of Civil and Environmental Engineering, University of Massachusetts, Amherst, MA, USA

    Casey Brown

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  1. Larissa Zaira Rafael Rolim
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Contributions

Conceptualization, F.A.S.F.; Methodology, L.Z.R.R. and F.A.S.F.; Validation, L.Z.R.R., F.A.S.F. and C.B; Formal Analysis, L.Z.R.R., F.A.S.F. and C.B.; Investigation, L.Z.R.R; Resources, F.A.S.F.; Writing-Original Draft Preparation, L.Z.R.R.; Writing-Review & Editing, L.Z.R.R., F.A.S.F. and C.B; Supervision, F.A.S.F.; Funding Acquisition, F.A.S.F.

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Correspondence to Larissa Zaira Rafael Rolim.

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Rolim, L.Z.R., de Souza Filho, F.d. & Brown, C. A Multi-model Framework for Streamflow Forecasting Based on Stochastic Models: an Application to the State Of Ceará, Brazil. Water Conserv Sci Eng 8, 7 (2023). https://doi.org/10.1007/s41101-023-00184-1

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