Shouke Wei

Dr. Shouke Wei

the School of Computer Science and Engineering, Jining University, Qufu, Shandong, China
orcid
Distinguished Professor, Jining Universit; Chief Scientist, ...

Research Focus & Metrics

Academic Career Length

20Years (2007-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 August 2012 in European Journal of Operational Research
Verified
Journal Article
Closed access
Shouke WeiShouke Wei
Shouke Wei
HYHong Yang
Hong Yang
JSJinxi Song
Jinxi Song
KAKarim C. Abbaspour
Karim C. Abbaspour
ZXZongxue Xu
Zongxue Xu

1 April 2010 in Water Research
Verified
Journal Article
Closed access
Shouke WeiShouke Wei
Shouke Wei
HYHong Yang
Hong Yang
KAKarim Abbaspour
Karim Abbaspour
SMS. Jamshid Mousavi
S. Jamshid Mousavi
AGAlbrecht Gnauck
Albrecht Gnauck

27 July 2011 in Hydrological Processes
Verified
Journal Article
Closed access
PDF file
Shouke WeiShouke Wei
Shouke Wei
JSJinxi Song
Jinxi Song
NKNasreen Islam Khan
Nasreen Islam Khan

18 February 2013 in Hydrological Sciences Journal
Verified
Journal Article
Open access
Shouke WeiShouke Wei
Shouke Wei
HYHong Yang
Hong Yang
JSJinxi Song
Jinxi Song
KAKarim Abbaspour
Karim Abbaspour
ZXZongxue Xu
Zongxue Xu
A wavelet-neural network (WNN) hybrid modelling approach for monthly river flow estimation and prediction is developed. This approach integrates discrete wavelet multi-resolution decomposition and a back-propagation (BP) feed-forward multilayer perceptron (FFML) artificial neural network (ANN). The Levenberg-Marquardt (LM) algorithm and the Bayesian regularization (BR) algorithm were employed to perform the network modelling. Monthly flow data from three gauges in the Weihe River in China were used for network training and testing for 48-month-ahead prediction. The comparison of results of the WNN hybrid model with those of the single ANN model show that the former is able to significantly increase the prediction accuracy. Editor D. Koutsoyiannis; Associate editor H. Aksoy Citation Wei, S., Yang, H., Song, J.X., Abbaspour, K., and Xu, Z.X., 2013. A wavelet-neural network hybrid modelling approach for estimating and predicting river monthly flows. Hydrological Sciences Journal, 58 (2), 374–389.

6 August 2014 in Hydrological Processes
Verified
Journal Article
Closed access
PDF file
DZDepeng Zuo
Depeng Zuo
ZXZongxue Xu
Zongxue Xu
Dingzhi PengDingzhi Peng
Dingzhi Peng
JSJinxi Song
Jinxi Song
Lei ChengLei Cheng
Lei Cheng
HYHong Yang
Hong Yang

13 June 2012 in Journal of Hydroinformatics
Verified
Journal Article
Closed access
PDF file
Shouke WeiShouke Wei
Shouke Wei
DZDepeng Zuo
Depeng Zuo
JSJinxi Song
Jinxi Song
This study developed a wavelet transformation and nonlinear autoregressive (NAR) artificial neural network (ANN) hybrid modeling approach to improve the prediction accuracy of river discharge time series. Daubechies 5 discrete wavelet was employed to decompose the time series data into subseries with low and high frequency, and these subseries were then used instead of the original data series as the input vectors for the designed NAR network (NARN) with the Bayesian regularization (BR) optimization algorithm. The proposed hybrid approach was applied to make multi-step-ahead predictions of monthly river discharge series in the Weihe River in China. The prediction results of this hybrid model were compared with those of signal NARNs and the traditional Wavelet-Artificial Neural Network hybrid approach (WNN). The comparison results revealed that the proposed hybrid model could significantly increase the prediction accuracy and prediction period of the river discharge time series in the current case study.