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Proceedings of the International Association of Hydrological Sciences An open-access publication for refereed proceedings in hydrology
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Volume 364
Proc. IAHS, 364, 100–105, 2014
https://doi.org/10.5194/piahs-364-100-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
Proc. IAHS, 364, 100–105, 2014
https://doi.org/10.5194/piahs-364-100-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

  16 Sep 2014

16 Sep 2014

Discrete wavelet transform coupled with ANN for daily discharge forecasting into Três Marias reservoir

C. A. G. Santos1, P. K. M. M. Freire1, G. B. L. Silva1, and R. M. Silva2 C. A. G. Santos et al.
  • 1Department of Civil and Environmental Engineering, Federal University of Paraíba, 58051-900 João Pessoa – PB, Brazil
  • 2Department of Geosciences, Federal University of Paraíba, 58051-900 João Pessoa – PB, Brazil

Keywords: Wavelet, ANN, forecasting

Abstract. This paper proposes the use of discrete wavelet transform (DWT) to remove the high-frequency components (details) of an original signal, because the noises generally present in time series (e.g. streamflow records) may influence the prediction quality. Cleaner signals could then be used as inputs to an artificial neural network (ANN) in order to improve the model performance of daily discharge forecasting. Wavelet analysis provides useful decompositions of original time series in high and low frequency components. The present application uses the Coiflet wavelets to decompose hydrological data, as there have been few reports in the literature. Finally, the proposed technique is tested using the inflow records to the Três Marias reservoir in São Francisco River basin, Brazil. This transformed signal is used as input for an ANN model to forecast inflows seven days ahead, and the error RMSE decreased by more than 50% (i.e. from 454.2828 to 200.0483).

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