Abstract
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.
Highlights
Dams can bring significant socio-economic benefits under safe operating conditions
Components separately, and overlay the prediction results of each component to complete the construction of the variational mode decomposition (VMD)-sample entropy (SE)-partial autocorrelation function (PACF)-extreme learning machine (ELM) model
Construction of the VMD-SE-PACF-ELM Model components separately, and overlay the prediction results of each component to complete the construction of the VMD-SE-PACF-ELM model
Summary
In case of a dam accident, there will be a huge disaster [1,2,3]. If we can establish suitable prediction models for dam monitoring data and analyze them in a timely manner, potential problems in the structural behavior of dams will be identified, avoiding accidents. As the controlled indicator of dam safety monitoring, deformation monitoring data can objectively reflect the structural state and the safety condition of dams, which are one of the important bases for assessing the safety of dam projects [5]. During the actual service of a dam, the deformation monitoring data are usually complex nonstationary and nonlinear time series. It is an important research topic to accurately predict dam deformation (DD) in the future by using historical
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