|Table of Contents|

Displacement Prediction of Landslide Based on SPA-SSA-SVR Model—Taking Bazimen and Baishuihe Landslides in Three Gorges Reservoir Area, China as Examples(PDF)

《地球科学与环境学报》[ISSN:1672-6561/CN:61-1423/P]

Issue:
2022年第06期
Page:
1096-1110
Research Field:
纪念刘国昌先生诞辰110周年专辑
Publishing date:

Info

Title:
Displacement Prediction of Landslide Based on SPA-SSA-SVR Model—Taking Bazimen and Baishuihe Landslides in Three Gorges Reservoir Area, China as Examples
Author(s):
YANG Ling;  WEI Jing*;  XU Zi-fu
(School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China)
Keywords:
displacement prediction;  landslide;  smoothness priors approach;  sparrow search algorithm;  support vector regression;  waveform similarity;  Three Gorges reservoir area
PACS:
P621.22
DOI:
10.19814/j.jese.2022.07043
Abstract:
In order to quantify the value of the regularization parameter in smoothness priors approach and improve the prediction accuracy of landslide displacement, a new method for predicting the landslide displacement based on smoothness priors approach(SPA)-sparrow search algorithm(SSA)-support vector regression(SVR)model was proposed; the Bazimen and Baishuihe landslides in Three Gorges reservoir area were taken as examples; the cumulative displacement-time curve was decomposed into fluctuation and trend terms by using SPA, the optimal regularization parameter of SPA was determined by analyzing the waveform similarity of the fluctuation term displacement and the influencing factor curves, then the optimal decomposition was obtained. SPA was also used to decompose the influencing factor sequences into stochastic and periodic terms, and the optimal influencing factor inputs sequences, when predicting the fluctuation term displacement was determined by calculating the correlation degree between the stochastic and fluctuation terms. The trend displacement was fitted by the BP neural network, and the fluctuation displacement was predicted by combating SSA and SVR model. Finally, the cumulative displacement was found to be the addition of the two component prediction values. The results show that the root mean square error(RMSE)of landslide displacement prediction for monitoring points ZG110 of Bazimen landslide based on SPA-SSA-SVR model is 4.32 mm, the RMSE values of monitoring points ZG118 and DX-01 in Baishuihe landslide are 3.44 and 4.81 mm, which are 8.59, 3.82 and 11.58 mm lower than the RMSE values by using empirical mode decomposition(EMD)-fruit fly optimization algorithm(FOA)-least square support vector machine(LSSVM)model, so the new method for predicting the landslide displacement is applicable; the optimal value of the regularization parameter in SPA tends to a fixed value with the increase of the cumulative displacement time series; the different monitoring points for the same landslide and the same type of landslide in the same area are basically the same regularization parameter, the optimal values in the regions of Bazimen and Baishuihe landslides are 6. The new method for predicting the landslide displacement based on SPA-SSA-SVR model can take into account the influence of landslide displacement decomposition to a certain extent, which provides ideas for the prediction of similar landslides.

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Last Update: 2022-11-25