DISPLACEMENT PREDICTION OF BAISHUIHE LANDSLIDE BASED ON MULTI ALGORITHM OPTIMIZATION AND SVR MODEL

Prediction of landslides is very important for mitigating geo-hazards but is scientifically very challenging. Based on the deformation characteristics and the data measured by monitoring of Baishuihe landslide, Three Gorges Reservoir, a time series addition model was established for the landslide displacement prediction. In the model, the accumulative displacement is divided into three parts:trend, periodic and random terms, which can be explained by the internal factors (geological environment, gravity, et al.), external factors (rainfall, reservoir water level, et al.), random factors (uncertainties).After statistically analyzing the displacement data, a cubic polynomial model was proposed for the trend term displacement prediction. And using multi algorithm to find the optimal support vector regression (SVR) model to train and predict the periodic term. The results show that based on Genetic algorithm (GA-SVR) and the time series model, the displacements prediction of Baishuihe landslide are better than the landslide displacements based on the particle swarm optimization (PSO-SVR) and the grid search (GS-SVR) model. Therefore, the coupling model based on the time series and GA-SVR can be used to predict the landslide displacement. GA-SVR will have broad application prospects in the prediction of the "step type" landslide displacement.