Abstract
A methodology to select the maximum level of wavelet decomposition to forecast seven days of daily inflows by a hybrid model wavelet-based artificial neural network (WANN) is proposed. The wavelet decomposition was employed to decompose an input time series into approximation and detail components, and the approximations were used as inputs to artificial neural networks (ANN) for WANN hybrid models. In this study, it was used daily inflows from January 1931 to December 2010 to three Brazilian reservoirs with different discharge patterns, and evaluated the accuracy of the WANN models when using seven different mother-wavelets, including Haar, Daubechies, Biorthogonal, Biorthogonal Reverse, Symlet, Coiflet and Discrete Meyer. It was found that the model performance is dependent on the input sets and the selected mother-wavelets. Based on the obtained results, it was observed that the maximum level of decomposition was five, because upper than this level, independently on the inflow magnitude, there is no guarantee that the WANN hybrid models would perform better than the ANN model.
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This study was funded by National Council for Scientific and Technological Development, Brazil (304213/2017–9). This study was also financed in part by the Brazilian Agency for the Improvement of Higher Education (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – CAPES) – Fund Code 001 and the Federal University of Paraíba.
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Freire, P.K.d.M., Santos, C.A.G. Optimal level of wavelet decomposition for daily inflow forecasting. Earth Sci Inform 13, 1163–1173 (2020). https://doi.org/10.1007/s12145-020-00496-z
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DOI: https://doi.org/10.1007/s12145-020-00496-z