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Wen chuan Wang
Researcher at North China University of Water Conservancy and Electric Power
Publications - 10
Citations - 1377
Wen chuan Wang is an academic researcher from North China University of Water Conservancy and Electric Power. The author has contributed to research in topics: Evolutionary computation & Time series. The author has an hindex of 7, co-authored 10 publications receiving 1120 citations. Previous affiliations of Wen chuan Wang include Dalian University of Technology.
Papers
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Improving Forecasting Accuracy of Annual Runoff Time Series Using ARIMA Based on EEMD Decomposition
TL;DR: Wang et al. as mentioned in this paper proposed an ensemble empirical mode decomposition (EEMD)-ARIMA model for forecasting annual runoff time series from Biuliuhe reservoir, Dahuofang reservoir and Mopanshan reservoir in China.
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Optimizing Hydropower Reservoir Operation Using Hybrid Genetic Algorithm and Chaos
TL;DR: A novel chaos genetic algorithm based on the chaos optimization algorithm (COA) and genetic algorithm (GA), which makes use of the ergodicity and internal randomness of chaos iterations, is presented to overcome premature local optimum and increase the convergence speed of genetic algorithm.
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Improved annual rainfall-runoff forecasting using PSO-SVM model based on EEMD
TL;DR: In this article, an adaptive data analysis methodology, ensemble empirical mode decomposition (EEMD), is presented for decomposing annual rainfall series in a rainfall-runoff model based on a support vector machine (SVM).
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Improving forecasting accuracy of medium and long-term runoff using artificial neural network based on EEMD decomposition.
TL;DR: EEMD can effectively enhance forecasting accuracy and the proposed EEMD-ANN model can attain significant improvement over ANN approach in medium and long-term runoff time series forecasting.
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Assessment of River Water Quality Based on Theory of Variable Fuzzy Sets and Fuzzy Binary Comparison Method
TL;DR: The results show that the proposed VFEM method can convey water cleanliness to certain degree by using the eigenvector of level H, which is much stricter in the superior level, and that it can improve the veracity for the assessment of water quality.