A novel mathematical model for predicting landslide displacement

Landslide displacement evolution is important for predicting landslide geological disasters. Because landslide displacement monitoring data are limited, in this paper we propose a novel model for predicting landslide displacement, namely the kernel grey model with fractional operators (FKGM). By combining the advantages of fractional modeling, kernel function methods and grey models, we derived the theoretical framework of FKGM. The parameters of FKGM were obtained using particle swarm optimization algorithm. Then, FKGM was applied in a case study of a landslide in Hubei, China. The engineering geological characteristics of the landslide were analyzed, and seven factors including rainfall and the rate of the reservoir water-level change were selected as inputs. The results show that the mean absolute percentage error and mean square error of FKGM are smaller than those of the least square support vector machine (LSSVM) and the classical grey prediction model—GM(1,1). The influence of the FKGM parameters was investigated. Our results indicate that FKGM can be applied to reliably predict large deformation of landslides.