Next Article in Journal
Perceptions and Acceptance of Desalinated Seawater for Irrigation: A Case Study in the Níjar District (Southeast Spain)
Next Article in Special Issue
Modelling Extreme Wave Overtopping at Aberystwyth Promenade
Previous Article in Journal
Evaluation of Double Perforated Baffles Installed in Rectangular Secondary Clarifiers
Previous Article in Special Issue
Estimation of Instantaneous Peak Flow Using Machine-Learning Models and Empirical Formula in Peninsular Spain
Article Menu
Issue 6 (June) cover image

Export Article

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

Streamflow Forecasting Using Empirical Wavelet Transform and Artificial Neural Networks

School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Hubei Key Laboratory of Digital Valley Science and Technology, Wuhan 430074, China
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]   |  


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

Figure 1

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).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

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

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Water EISSN 2073-4441 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top