PSO-SVM-based deep displacement prediction of Majiagou landslide considering the deformation hysteresis effect

• Published in: Landslides Vol. 18; no. 1; pp. 179 - 193
• Main Authors: Zhang, Lei, Shi, Bin, Zhu, Honghu, Yu, Xiong Bill, Han, Heming, Fan, Xudong
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 2021; Springer Nature B.V
• Subjects: Accuracy; Agriculture; Bragg gratings; Canyons; Casualties; Civil Engineering; Correlation analysis; Current prediction; Deformation; Deformation effects; Displacement; Dynamic analysis; Earth and Environmental Science; Earth Sciences; Economic impact; Economics; Geography; Hysteresis; Inclinometers; Lag time; Landslide effects; Landslides; Learning algorithms; Machine learning; Model accuracy; Natural Hazards; Original Paper; Particle swarm optimization; Prediction models; Predictions; Rain; Rainfall; Reservoir water; Reservoirs; Root-mean-square errors; Slope indicators; Support vector machines; Time measurement; Water level fluctuations; Water levels; Deformation hysteresis effect; PSO-SVM; Reservoir landslide; Set pair analysis (SPA); Displacement prediction; Fiber Bragg grating (FBG)
• ISSN: 1612-510X; 1612-5118
• DOI: 10.1007/s10346-020-01426-2

The accuracy of landslide displacement prediction can effectively prevent casualties and economic losses. To achieve accurate prediction of the Majiagou landslide displacement in the Three Gorges Reservoir (TGR), China, a hybrid machine learning prediction model considering the deformation hysteresis effect is proposed. The real-time deep displacement measurements were captured by using in-place inclinometers with Fiber Bragg grating (FBG) sensors. The time series method was adopted to divide the total displacement into a trend term and periodic term. Trend displacement was determined by the geological condition and predicted by the fitting method. Periodic displacement was controlled by external factors such as rainfall and fluctuation of reservoir water level. Before making the prediction, the grey correlation analysis was adopted to confirm that the fluctuation of the reservoir water level was the main influence factor. In view of the deficiency that current prediction methods could not quantitatively determine the lag time of landslide deformation and thus select the influencing factors empirically, the dynamic analysis of the correlation between periodic influence factors and periodic displacement was carried out in this paper, and the deformation lag time was identified to be 18 days by using set pair analysis (SPA) method. Finally, the optimal influence factors were selected and the prediction model of Majiagou landslide based on support vector machine optimized by particle swarm optimization (SPA-PSO-SVM) was established. Results showed that the root mean square error (RMSE) and the mean absolute percentage error (MAPE) of the proposed SPA-PSO-SVM prediction model are 0.28 and 12.8, respectively. Compared with the PSO-SVM model, the prediction accuracy of the proposed model had been improved significantly. The reliability and effectiveness of the SPA-PSO-SVM prediction model is verified and it has apparent advantages while predicting landslide displacement with deformation hysteresis effect involved.