[1]LIU Yang,WANG Lihu,YANG Libo,et al.Application of improved EMD-GRU hybrid model in runoff forecasting[J].CAAI Transactions on Intelligent Systems,2022,17(3):480-487.[doi:10.11992/tis.202105010]
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Application of improved EMD-GRU hybrid model in runoff forecasting

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 17 Number of periods: 2022 3 Page number: 480-487 Column: 学术论文—机器学习 Public date: 2022-05-05
Title:
Application of improved EMD-GRU hybrid model in runoff forecasting
Author(s):
LIU Yang;  WANG Lihu;  YANG Libo;  LIU Xuemei
School of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
Keywords:
runoff prediction; ensemble empirical mode decomposition; deep learning; gated recurrent unit; parallel computing; hybrid model; time series prediction; engineering application
CLC:
TP183;TV124
DOI:
10.11992/tis.202105010
Abstract:
Runoff prediction models suffer from low prediction accuracy, poor stability, and high delay. Combined with the advantages of gate control recurrent unit (GRU) neural network and ensemble empirical mode decomposition (EEMD), we propose a deep learning model based on the improved EEMD method (EEMD-GRU) to solve the aforementioned problems of runoff models. Our model initially uses an intelligent algorithm to extend the boundary of the runoff signal to solve the EEMD boundary effect. Subsequently, the improved EEMD method is used to decompose the runoff signals into numerous steady-state components. Each component is then used as the input in the GRU model and is predicted. The experimental results show that the prediction accuracy, credibility, and efficiency of the parallel EEMD-GRU runoff prediction model improve by 82.50%, 144.67%, and 95.49%, respectively, in comparison to the support vector regression model combined with empirical mode decomposition. The optimal calculation results based on EEMD-GRU show that this method can further reduce the economic loss of regional flood control and improve the efficiency of disaster supervision.
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