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- Title
基于EEMD与ANN混合方法的水库月径流预测.
- Authors
王佳; 王旭; 王浩; 雷晓辉; 谭乔凤; 徐意
- Abstract
In order to improve the prediction accuracy, ensemble empirical mode decomposition (EEMD) and artificial neural network (ANN) hybrid model was proposed for monthly runoff forecasting, which considered runoff series complicated non-stationary characteristics. Firstly, in order to achieve steady state of runoff series, the non-linear and non-stationary runoff series was decomposed into several intrinsic mode functions (IMFs) and a trend series by using EEMD. Then, ANN model was established for different IMFs and the trend series respectively. Lastly, reconstructing forecast runoff sequence by superimposing all forecasting model. Taking Longyangxia (LYX) Reservoir of the Yellow River as an example, the inflow was predicted based on EEMD-ANN model. The results indicated that the hybrid method could accurately forecast the inflow of LYX Reservoir. At the same time, through comparison continuous and adaptive forecast and runoff series of same month forecast based on EEMD-ANN, it was found that the former method was better in flood season for forecasting monthly runoff, while runoff series of same month forecast method could be used to improve the accuracy of runoff prediction in dry season.
- Publication
Yellow River, 2019, Vol 41, Issue 5, p47
- ISSN
1000-1379
- Publication type
Article
- DOI
10.3969/j.issn.1000-1379.2019.05.010