Study on deformation prediction of gravel soil landslides based on CE-EMD-GWO-RVM-CT model

Abstract

[Objective] To achieve high-precision deformation prediction of crushed stone soil landslides, [Methods] based on the result of landslide exploration and deformation monitoring, multi-scale variable identification of deformation data is first achieved using complementary set empirical modes. Then, a deformation prediction model is constructed using Grey Wolf optimization algorithm, correlation vector machine, and chaos theory to grasp the deformation characteristics of landslides and guide their disaster prevention and control. [Results] The result show that the optimization of the complementary set empirical mode in the construction process can reasonably improve the recognition effect and effectively identify multi-scale variables of landslide deformation data. The combination optimization steps of the prediction process can effectively improve the prediction accuracy,and in the final prediction result of the four monitoring points, the average relative error ranges from 1. 96% to 2. 20%, and the variance value ranges from 0. 002 0 to 0. 003 5, fully demonstrating the strong robustness of the prediction model. [Conclusion] The result indicate that the prediction model considering multi-scale variable identification of deformation data is suitable for predicting the deformation of gravel soil landslides. According to the comparison of its extrapolation prediction result, the existing deformation rate of C1 monitoring point is relatively higher, and the prediction rate of the other three monitoring points is relatively higher. It indicates that the stability of the landslide's trailing edge will tend to be stable, but the stability of its leading edge position will tend to be unfavorable. It is recommended to carry out prevention and control measures for this landslide as soon as possible.

Keywords

gravel soil landslide / multi scale variables / empirical mode / deformation prediction / relevance vector machine / landslides