A novel grey multivariate model for forecasting landslide displacement

Forecasting landslide displacement is an important issue in engineering geology. In this field, it is difficult to accurately forecast the displacement of the step point of a step-type landslide. In this study, a novel grey multivariate model is proposed for forecasting landslide displacement. The proposed model transforms the original sequence by using the Hausdorff derivative operator, determines the model parameters by using the particle swarm optimization algorithm, and uses the trapezoidal integral formula to calculate the predicted value. Two numerical examples show that the average absolute relative error and mean squared error of the proposed model are smaller than those of the recursive discrete multivariate grey model and the multivariable grey model with structure compatibility. The proposed model is used to forecast the displacement of the Bazimen landslide in the Three Gorges Reservoir area of China. The previous month's displacement, precipitation, and change in the reservoir water level are used as input variables. The results show that the performance of the proposed model is superior to that of the extreme learning machine model. This paper provides an effective method for forecasting displacement of the step point of a step-type landslide.

Automated summary

A novel grey multivariate model is proposed for forecasting landslide displacement. The model outperforms other common models in terms of prediction accuracy and effectiveness, providing an effective method for forecasting landslide displacement.