Abstract
Streamflow forecasting is a fundamental tool in water resource studies. If information on the nature of the inflow is determinable in advance, then a given reservoir can be operated by some decision rule to minimize downstream flood damage and maximize the generated power with low costs. However, traditional methods such as linear time series models do not model the series properly, ignoring its dynamical behavior. This paper provides a method based on the Reservoir Computing (RC) technique combined with trend information extracted from the series for short-term streamflow forecasting. The model was tested in five hydroelectric plants located in different river basins in Brazil. Experimental results show that the proposed method is able to achieve better generalization performance than the traditional methods.
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Bezerra, S.G.T.A., de Andrade, C.B., Valença, M.J.S. (2016). Using Reservoir Computing and Trend Information for Short-Term Streamflow Forecasting. In: Villa, A., Masulli, P., Pons Rivero, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2016. ICANN 2016. Lecture Notes in Computer Science(), vol 9887. Springer, Cham. https://doi.org/10.1007/978-3-319-44781-0_37
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DOI: https://doi.org/10.1007/978-3-319-44781-0_37
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