Displacement prediction model of landslide based on time series and GWO-ELM

Considering the landslide displacement characteristics of the Three Gorges Reservoir Area, a displacement prediction model based on time series and Extreme Learning Machine with Grey Wolves Optimization(GWO-ELM) was proposed to predict the Baishuihe Landslide. Firstly, based on the intrinsic evolution of landslides and external factors, a time series model of landslide prediction was established. The monitoring displacement was decomposed into trend displacement and periodic displacement, and the trend displacement was fitted by a cubic polynomial with a robust weighted least square method to obtain a periodic displacement. Secondly, the periodic displacement was predicted respectively by the GWO-ELM, the separate ELM and the GWO-SVM model through analyzing the influencing factors. The results show that the GWO-ELM prediction model has good generalization ability and it can reduce human error effectively. In terms of the prediction accuracy, GWO-ELM prediction model is apparently more precise than the ELM and GWO-SVM models. Based on the time series and the GWO-ELM model, the proposed model embodies a higher prediction accuracy and has generalization ability, so it is an effective landslide displacement prediction method. © 2019, Central South University Press. All right reserved.