Landslide Displacement Prediction Model Using Time Series Analysis Method and Modified LSTM Model

Landslides are serious and complex geological and natural disasters that threaten the safety of people’s health and wealth worldwide. To face this challenge, a landslide displacement prediction model based on time series analysis and modified long short-term memory (LSTM) model is proposed in this paper. Considering that data from different time periods have different time values, the weighted moving average (WMA) method is adopted to decompose the cumulative landslide displacement into the displacement trend and periodic displacement. To predict the displacement trend, we combined the displacement trend of landslides in the early stage with an LSTM model. Considering the repeatability and periodicity of rainfall and reservoir water level in every cycle, a long short-term memory fully connected (LSTM-FC) model was constructed by adding a fully connected layer to the traditional LSTM model to predict periodic displacement. The two predicted displacements were added to obtain the final landslide predicted displacement. In this paper, under the same conditions, we used a polynomial function algorithm to compare and predict the displacement trend with the LSTM model and used the LSTM-FC model to compare and predict the displacement trend with eight other commonly used algorithms. Two prediction results indicate that the modified prediction model is able to effectively predict landslide displacement.