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Forecasting slope deformation field using correlated grey model updated with time correction factor and background value optimization

Zhang, Wei, Xiao, Rui, Shi, Bin, Zhu, Hong-hu, Sun, Yi-jie
Engineering geology 2019 v.260 pp. 105215
algorithms, deformation, engineering, landslides, models, monitoring, multivariate analysis, prediction, time series analysis
Developing precise models to characterize the slope instability mechanism quantitatively and predict slope movement behavior accurately remains a challenge in engineering geology. This paper updates the traditional GM (1,1) grey model to the TGM (1,1,p,q) grey model by adding a nonlinear time correction factor and optimizing the weighting coefficients of the background values using Genetic Algorithm. Furthermore, the TGM (1,1,p,q) has been updated to the TGM (1,n,p,q) correlated grey model by using a multivariate model of multiple monitoring boreholes to characterize the integrated deformation field. Taking the Majiagou reservoir bank landslide as an example, the time-series data of five monitoring boreholes were compared with their predicted values. The results show that the prediction accuracy of the updated grey model TGM (1,1,p,q) is acceptable, and the accuracy of the correlated updated grey model TGM (1,n,p,q) is much better. TGM (1,n,p,q) holds the potential of attaining a satisfactory prediction accuracy for the landslide deformation field forecasting.