The Application of Long Short-Term Memory(LSTM) Method on Displacement Prediction of Multifactor-Induced Landslides

Abstract

Landslides induced by rainfall frequently happen in South-western China where steep slopes, loess plateau occur. Thus, it is empirical to build the early warning system to evaluate the potential of landslide hazards. However, current researches mostly focus the static model on displacement prediction. The landslide is a nonlinear hazard characterized by dynamic features. Therefore, the dynamic model should be investigated to more precisely predict the displacement associated with the landslide. In this paper, Laowuji Landslide is adopted to investigate the dynamic failure mode. The displacement of the Laowuji landslide contains the trend and periodic component. The trend component is predicted by the empirical mode decomposition and the periodic component is predicted by the long short-term memory (LSTM) method. Model's input includes multiple factors of geological conditions, rainfall intensity, and human activities. The measured data and the predicted data show good consistency. In addition, the predicted results of the periodic component show that the performance of the LSTM has good characteristics of dynamic feature. Compared with a traditional mechanical model, the hybrid model is more powerful to predict the landslide displacement triggered by multiplying factors.