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

• 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
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs16213978

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.