Mix variational mode decomposition long short-term memory forpredicting of reservoir surface displacement and deformation
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1.School of Surveying and Geoinformation Engineering, East China University of Technology, Nanchang 330013 , China ;2.School of Civil and Mapping Engineering, Jiangxi University of Science and Technology, Ganzhou 341000 , China ; 3.Hebei Institute of Investigation and Design of Water Conservancy and Hydropower Co.,Ltd., Shijiazhuang 050085 , China ; 4.Hebei Water Conservancy Engineering Bureau Group Limited, Shijiazhuang 050021 , China

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P228

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    Abstract:

    In order to improve the prediction accuracy of the displacement and deformation of reservoir, the displacement and deformation of non-linear and non-stationary reservoir was predicted by changing the decomposition method of VMD(variational mode decomposition) and integrating VMD and long short-term memory. A MVMDLSTM (mixed variational mode decomposition long short-term memory) model prediction method was proposed. The reliability of the new method was verified with multi-source datasets for different single prediction models and combined models.The experimental results show that the MVMDLSTM model can effectively attenuate the bias of the single prediction model and the empirical mode decomposition combination model estimation, and the prediction accuracy of the MVMDLSTM model is better, which provides an effective data decision-making for the stable monitoring of the prediction and warning of the reservoirs slow sliding and creeping and other small deformations.

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孙喜文, 贺小星, 鲁铁定, 等. 混合变分模态长短期记忆网络水库表面位移形变预测[J]. 国防科技大学学报, 2025, 47(3): 151-161.

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  • Received:April 10,2023
  • Revised:
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  • Online: June 03,2025
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