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Open AccessArticle

A Novel Hybrid Decomposition—Ensemble Prediction Model for Dam Deformation

by Enhua Cao 1,2,3, Tengfei Bao 1,2,3,*, Chongshi Gu 1,2,3, Hui Li 1,2,3, Yongtao Liu 1,2,3 and Shaopei Hu 1,2,3
1
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China
2
College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China
3
National Engineering Research Center of Water Resources Efficient Utilization and Engineering Safety, Hohai University, Nanjing 210098, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(16), 5700; https://doi.org/10.3390/app10165700
Received: 16 July 2020 / Revised: 7 August 2020 / Accepted: 14 August 2020 / Published: 17 August 2020
(This article belongs to the Section Civil Engineering)
Accurate and reliable prediction of dam deformation (DD) is of great significance to the safe and stable operation of dams. In order to deal with the fluctuation characteristics in DD for more accurate prediction results, a new hybrid model based on a decomposition-ensemble model named VMD-SE-ER-PACF-ELM is proposed. First, the time series data are decomposed into subsequences with different frequencies and an error sequence (ER) by variational mode decomposition (VMD), and then the secondary decomposition method is introduced into the prediction of ER. In these two decomposition processes, the sample entropy (SE) method is innovatively utilized to determine the decomposition modulus. Then, the input variables of the subsequences are selected by partial autocorrelation analysis (PACF). Finally, the parameter-optimization-based extreme learning machine (ELM) models are used to predict the subsequences, and the outputs are reconstructed to obtain the final prediction results. The case analysis shows that the VMD-SE-ER-PACF-ELM model has strong prediction ability for DD. The model is then compared with other nonlinear and time series models, and its performance under different prediction periods is also analyzed. The results show that the proposed model is able to adequately describe the original DD. It performs well in both training and testing stages. It is a preferred data-driven model for DD prediction and can provide a priori knowledge for health monitoring of dams. View Full-Text
Keywords: dam deformation prediction; VMD; decomposition modulus selection; secondary decomposition; extreme learning machine dam deformation prediction; VMD; decomposition modulus selection; secondary decomposition; extreme learning machine
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Cao, E.; Bao, T.; Gu, C.; Li, H.; Liu, Y.; Hu, S. A Novel Hybrid Decomposition—Ensemble Prediction Model for Dam Deformation. Appl. Sci. 2020, 10, 5700.

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