High slopes are susceptible to rainfall, weathering, earthquakes and human engineering construction, resulting in the originally stable high slopes are very prone to landslides and collapses, etc. In order to prevent such phenomena, in addition to optimizing the existing monitoring system, it is also crucial to establish a scientific and accurate prediction model for high slopes. In order to improve the prediction accuracy of high slope deformation data, it is proposed to establish a deformation data prediction model coupled with autoregressive integrated moving average model (ARIMA model), genetic algorithm (GA) and BP neural network. The model takes into account the linear and nonlinear parts of the data, and the ARIMA model is used for linear prediction, while the GA-BP algorithm regressively corrects the residuals of the ARIMA model prediction for nonlinear prediction. The results show that the residual-corrected ARIMA-GA-BP model has higher accuracy and extrapolation ability than other prediction models, and can be effectively applied in the research field of high slope deformation prediction.
KEYWORDS: Deformation, Data modeling, Noise cancelling, Wavelets, Denoising, Interference (communication), Singular value decomposition, Signal to noise ratio, Modeling, Modal decomposition
Aiming at the uncertainty of high slope deformation data and the problem that the actual deformation trend is easy to be overwhelmed by strong noise, this paper proposes an algorithm based on the coupling of set-averaged empirical modal decomposition (MEEMD) and singular value decomposition (SVD) to realize the noise cancellation of deformation data, firstly, the low-frequency flickering noise is rejected from the deformation data by using MEEMD decomposition to realize the primary filtering of the signal; and then, the SVD decomposition is performed on the obtained IMF components separately to reject the high-frequency white noise of each IMF component. Then, the SVD decomposition of each IMF component is carried out to eliminate the high-frequency white noise of each IMF component to realize the secondary filtering of the signal. The results show that the model has significant noise reduction effect and can fully describe the intrinsic detail information of the deformation data, which provides a fruitful method for future research on the processing of deformation monitoring data.
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