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Water 2017, 9(6), 406; doi:10.3390/w9060406

Streamflow Forecasting Using Empirical Wavelet Transform and Artificial Neural Networks

1
School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
2
Hubei Key Laboratory of Digital Valley Science and Technology, Wuhan 430074, China
3
School of Hydraulic and Environmental Engineering, Three Gorges University, Yichang 443002, China
*
Author to whom correspondence should be addressed.
Academic Editors: Yunqing Xuan, Harshinie Karunarathna and Adrián Pedrozo-Acuña
Received: 13 April 2017 / Revised: 31 May 2017 / Accepted: 2 June 2017 / Published: 7 June 2017
View Full-Text   |   Download PDF [3867 KB, uploaded 7 June 2017]   |  

Abstract

Accurate and reliable streamflow forecasting plays an important role in various aspects of water resources management such as reservoir scheduling and water supply. This paper shows the development of a novel hybrid model for streamflow forecasting and demonstrates its efficiency. In the proposed hybrid model for streamflow forecasting, the empirical wavelet transform (EWT) is firstly employed to eliminate the redundant noises from the original streamflow series. Secondly, the partial autocorrelation function (PACF) values are explored to identify the inputs for the artificial neural network (ANN) models. Thirdly, the weights and biases of the ANN architecture are tuned and optimized by the multi-verse optimizer (MVO) algorithm. Finally, the simulated streamflow is obtained using the well-trained MVO-ANN model. The proposed hybrid model has been applied to annual streamflow observations from four hydrological stations in the upper reaches of the Yangtze River, China. Parallel experiments using non-denoising models, the back propagation neural network (BPNN) and the ANN optimized by the particle swarm optimization algorithm (PSO-ANN) have been designed and conducted to compare with the proposed model. Results obtained from this study indicate that the proposed hybrid model can capture the nonlinear characteristics of the streamflow time series and thus provides more accurate forecasting results. View Full-Text
Keywords: empirical wavelet transform; multi-verse optimizer; artificial neural networks; annual streamflow forecasting; data-driven modeling empirical wavelet transform; multi-verse optimizer; artificial neural networks; annual streamflow forecasting; data-driven modeling
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Peng, T.; Zhou, J.; Zhang, C.; Fu, W. Streamflow Forecasting Using Empirical Wavelet Transform and Artificial Neural Networks. Water 2017, 9, 406.

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