Displacement prediction of landslide based on generalized regression neural networks with K-fold cross-validation

In this paper, we propose a generalized regression neural networks (GRNNS) with K-fold cross-validation (GRNNSK) method for predicting the displacement of landslide. Furthermore, correlation analysis is used to find the potential input variables for this predicting model, such as Pearson cross-correlation coefficients (PCC) and mutual information (MI) are applied in this paper. Tests on two case studies of Liangshuijing (LSJ) and Baishuihe (BSH) landslide in the Three Gorges reservoir area of China demonstrate the effectiveness of the proposed approach.