Vol15 No.1: 156-166
【Title】A WD-GA-LSSVM model for rainfall-triggered landslide displacement prediction
【Author】ZHU Xing1,2; MA Shu-qi2*; XU Qiang1*; LIU Wen-de1
【Addresses】1 State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China; 2 School of Civil and Environmental Engineering, Nanyang Technological University, Singapore 637787, Singapore
【Citation】Zhu X, Ma SQ, Xu Q, et al. (2018) A WD-GA-LSSVM model for rainfall-triggered landslide displacement prediction. Journal of Mountain Science 15(1). https://doi.org/10.1007/s11629-016-4245-3
【Abstract】This paper proposes a WD-GA-LSSVM model for predicting the displacement of a deep-seated landslide triggered by seasonal rainfall, in which wavelet denoising (WD) is used in displacement time series of landslide to eliminate the GPS observation noise in the original data, and genetic algorithm (GA) is applied to obtain optimal parameters of least squares support vector machines (LSSVM) model. The model is first trained and then evaluated by using data from a gentle dipping (~2°-5°) landslide triggered by seasonal rainfall in the southwest of China. Performance comparisons of WD-GA-LSSVM model with Back Propagation Neural Network (BPNN) model and LSSVM are presented, individually. The results indicate that the adoption of WD-GA-LSSVM model significantly improves the robustness and accuracy of the displacement prediction and it provides a powerful technique for predicting the displacement of a rainfall-triggered landslide.
【Keywords】WD-GA-LSSVM; Landslide；Rainfall；Displacement prediction；Wavelet denoising