A Novel Hybrid Decomposition—Ensemble Prediction Model for Dam Deformation

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.

A novel framework for prediction of dam deformation based on extreme learning machine and Lévy flight bat algorithm

“…Unlike most studies (Cao et al 2020), we applied the combined two functions as the kernel function of KELM. We performed the optimization comparison between PG kernel function, polynomial kernel, and RBF kernel function (Tables 3 and 4; Figures 5 and 6).…”

“…Finally, due to the random mapping of ELM, even with the same set of inputs, the outputs will be different (Barzegar et al 2016). The kernel extreme learning machine can improve the stability and accuracy of the prediction, and can overcome the limitations of random mapping of ELM (Cao et al 2020). Kang et al (2017) proposed a prediction model based on Gaussian kernel function and extreme learning machine.…”

3D Finite Element Analysis of a Concrete Dam Behavior under Changing Hydrostatic Load: A Case Study

“…In addition to the temperature load, the hydrostatic pressure causes high stresses in large arch-gravity concrete dam structures and has consequently great influence on cracks and dam deformation [ 1 ]. Accurate and reliable prediction of dam deformation is of great importance to ensure the safe and stable operation of dams [ 2 ]. Due to climate change, the frequent extreme rainfall can cause a sudden rise of the water level of the reservoir, which can endanger the dam safety [ 3 ].…”

Exploration of Multi-Scale Reconstruction Framework in Dam Deformation Prediction

“…Furthermore, how to improve the prediction accuracy is an important issue in the field of the dam's safety monitoring [3]. From the perspective of the current relevant research, the common dam deformation analysis and prediction models are mainly divided into the following three categories: the statistical model [4][5][6], the deterministic model [7,8], and the mixed model [9][10][11].…”