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Publishing Language: Chinese | Open Access

Mix variational mode decomposition long short-term memory for predicting of reservoir surface displacement and deformation

Xiwen SUN1Xiaoxing HE2( )Tieding LU1Haicheng WANG3Yuntao ZHANG4Hongkang CHEN1
School of Surveying and Geoinformation Engineering, East China University of Technology, Nanchang 330013, China
School of Civil and Mapping Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China
Hebei Institute of Investigation and Design of Water Conservancy and Hydropower Co., Ltd., Shijiazhuang 050085, China
Hebei Water Conservancy Engineering Bureau Group Limited, Shijiazhuang 050021, China
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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 reservoir′s slow sliding and creeping and other small deformations.

CLC number: P228 Document code: A Article ID: 1001-2486(2025)03-151-11

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Journal of National University of Defense Technology
Pages 151-161

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Cite this article:
SUN X, HE X, LU T, et al. Mix variational mode decomposition long short-term memory for predicting of reservoir surface displacement and deformation. Journal of National University of Defense Technology, 2025, 47(3): 151-161. https://doi.org/10.11887/j.cn.202503016

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Received: 10 April 2023
Published: 25 July 2025
© 2025 Journal of National University of Defense Technology

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).