Analysis of the use of discrete wavelet transforms coupled with ANN for short-term streamflow forecasting

Abstract The use of wavelet transforms to forecast daily streamflows into the Sobradinho Reservoir (Bahia State, Brazil) seven days ahead by a wavelet-artificial neural network (ANN) hybrid system was analyzed in this paper. This work also determined the appropriate mother-wavelet for this type of forecasting with an ANN, performed 1836 simulations with the wavelet-ANN hybrid systems (tested with 54 mother-wavelets) and compared the results with the predictions made without the application of a wavelet transform (henceforth called a stand-alone ANN). Daily data from January 1931 to December 2010 were used. According to the results, the wavelet-ANN hybrid system performed better than the system using the ANN with the raw data. The approximation A 3 from the discrete Meyer mother-wavelet obtained the best results; the root mean square error (RMSE) decreased by approximately 80%, while the R 2 and NASH coefficients increased by more than 5% and 10%, respectively, compared with the stand-alone ANN.

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