Journal
HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES
Volume 63, Issue 15-16, Pages 2060-2075Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/02626667.2018.1552788
Keywords
monthly streamflow forecasting; artificial neural networks; wavelet transform; low frequency
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Funding
- National Council for Scientific and Technological Development, Brazil (CNPq) [471342/2012-2, 304213/2017-9]
- Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior, Brazil (CAPES) [001]
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Combinations of low-frequency components (also known as approximations) resulting from the wavelet decomposition are tested as inputs to an artificial neural network (ANN) in a hybrid approach, and compared to classical ANN models for flow forecasting for 1, 3, 6 and 12 months ahead. In addition, the inputs are rewritten in terms of the flow, revealing what type of information was being provided to the network, in order to understand the effect of the approximations on the forecasting performance. The results show that the hybrid approach improved the accuracy of all tested models, especially for 1, 3 and 6 months ahead. The input analyses show that high-frequency components are more important for shorter forecast horizons, while for longer horizons, they may worsen the model accuracy.
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