Daily streamflow forecasting using a wavelet transform and artificial neural network hybrid models |
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| Authors: | Celso Augusto Guimarães Santos Gustavo Barbosa Lima da Silva |
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| Institution: | 1. Department of Civil and Environmental Engineering, Federal University of Paraíba, 58051-900 Jo?o Pessoa, Paraíba, Brazilcelso@ct.ufpb.br;3. Department of Civil and Environmental Engineering, Federal University of Paraíba, 58051-900 Jo?o Pessoa, Paraíba, Brazil |
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| Abstract: | AbstractNew wavelet and artificial neural network (WA) hybrid models are proposed for daily streamflow forecasting at 1, 3, 5 and 7 days ahead, based on the low-frequency components of the original signal (approximations). The results show that the proposed hybrid models give significantly better results than the classical artificial neural network (ANN) model for all tested situations. For short-term (1-day ahead) forecasts, information on higher-frequency signal components was essential to ensure good model performance. However, for forecasting more days ahead, lower-frequency components are needed as input to the proposed hybrid models. The WA models also proved to be effective for eliminating the lags often seen in daily streamflow forecasts obtained by classical ANN models. Editor D. Koutsoyiannis; Associate editor L. SeeCitation Santos, C.A.G. and Silva, G.B.L., 2013. Daily streamflow forecasting using a wavelet transform and artificial neural network hybrid models. Hydrological Sciences Journal, 59 (2), 312–324. |
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| Keywords: | discharge prediction time series signal decomposition |
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