A New Algorithm for Predicting Dam Deformation Using Grey Wolf-Optimized Variational Mode Long Short-Term Neural Network

To solve the problems of difficult to model parameter selections, useful signal extraction and improper-signal decomposition in nonlinear, non-stationary dam displacement time series prediction methods, we propose a new predictive model for grey wolf optimization and variational mode decomposition a...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2024-11, Vol.16 (21), p.3978
Main Authors: Sun, Xiwen, Lu, Tieding, Hu, Shunqiang, Wang, Haicheng, Wang, Ziyu, He, Xiaoxing, Ding, Hongqiang, Zhang, Yuntao
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Analysis
Animal populations
Artificial intelligence
dam deformation prediction
Dam stability
Decomposition
Deep learning
Deformation effects
grey wolf optimization
Hunting
Lagrange multiplier
Long short-term memory
Neural networks
Optimization
Parameters
Permutations
Prediction models
Predictions
Support vector machines
Time series
variational mode decomposition
Wolves
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Summary:To solve the problems of difficult to model parameter selections, useful signal extraction and improper-signal decomposition in nonlinear, non-stationary dam displacement time series prediction methods, we propose a new predictive model for grey wolf optimization and variational mode decomposition and long short-term memory (GVLSTM). Firstly, we used the grey wolf optimization (GWO) algorithm to optimize the parameters of variable mode decomposition (VMD), obtaining the optimal parameter combination. Secondly, we used multiscale permutation entropy (MPE) as a standard to select signal screening, determining and recon-structing the effective modal components. Finally, the long short-term memory neural network (LSTM) was used to learn the dam deformation characteristics. The result shows that the GVLSTM model can effectively reduce the estimation deviation of the prediction model. Compared with VMDGRU and VMDANN, the average RMSE and MAE value of each station is increased by 19.11%~28.58% and 27.66%~29.63%, respectively. We used determination (R2) coefficient to judge the performance of the prediction model, and the value of R2 was 0.95~0.97, indicating that our method has good performance in predicting dam deformation. The proposed method has outstanding advantages of high accuracy, reliability, and stability for dam deformation prediction.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs16213978