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- Title
Optimal level of wavelet decomposition for daily inflow forecasting.
- Authors
Freire, Paula Karenina de Macedo Machado; Santos, Celso Augusto Guimarães
- 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.
- Subjects
ARTIFICIAL neural networks; TIME series analysis; BIORTHOGONAL systems
- Publication
Earth Science Informatics, 2020, Vol 13, Issue 4, p1163
- ISSN
1865-0473
- Publication type
Article
- DOI
10.1007/s12145-020-00496-z