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Water 2018, 10(5), 628; https://doi.org/10.3390/w10050628

Data Pre-Analysis and Ensemble of Various Artificial Neural Networks for Monthly Streamflow Forecasting

1,2,* , 1,2,* , 1,2
and
1,2
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
*
Authors to whom correspondence should be addressed.
Received: 28 March 2018 / Revised: 28 April 2018 / Accepted: 7 May 2018 / Published: 13 May 2018
(This article belongs to the Special Issue Flood Forecasting Using Machine Learning Methods)
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Abstract

This paper introduces three artificial neural network (ANN) architectures for monthly streamflow forecasting: a radial basis function network, an extreme learning machine, and the Elman network. Three ensemble techniques, a simple average ensemble, a weighted average ensemble, and an ANN-based ensemble, were used to combine the outputs of the individual ANN models. The objective was to highlight the performance of the general regression neural network-based ensemble technique (GNE) through an improvement of monthly streamflow forecasting accuracy. Before the construction of an ANN model, data preanalysis techniques, such as empirical wavelet transform (EWT), were exploited to eliminate the oscillations of the streamflow series. Additionally, a theory of chaos phase space reconstruction was used to select the most relevant and important input variables for forecasting. The proposed GNE ensemble model has been applied for the mean monthly streamflow observation data from the Wudongde hydrological station in the Jinsha River Basin, China. Comparisons and analysis of this study have demonstrated that the denoised streamflow time series was less disordered and unsystematic than was suggested by the original time series according to chaos theory. Thus, EWT can be adopted as an effective data preanalysis technique for the prediction of monthly streamflow. Concurrently, the GNE performed better when compared with other ensemble techniques. View Full-Text
Keywords: monthly streamflow forecasting; artificial neural network; ensemble technique; phase space reconstruction; empirical wavelet transform monthly streamflow forecasting; artificial neural network; ensemble technique; phase space reconstruction; empirical wavelet transform
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Zhou, J.; Peng, T.; Zhang, C.; Sun, N. Data Pre-Analysis and Ensemble of Various Artificial Neural Networks for Monthly Streamflow Forecasting. Water 2018, 10, 628.

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