Landslide Surface Displacement Prediction Based on VSXC-LSTM Algorithm
Menglin Kong Ruichen Li Fan Liu Xingquan Li Juan Cheng Muzhou Hou Cong Cao
Landslide is a natural disaster that can easily threaten local ecology,
people's lives and property. In this paper, we conduct modelling research on
real unidirectional surface displacement data of recent landslides in the
research area and propose a time series prediction framework named
VMD-SegSigmoid-XGBoost-ClusterLSTM (VSXC-LSTM) based on variational mode
decomposition, which can predict the landslide surface displacement more
accurately. The model performs well on the test set. Except for the random item
subsequence that is hard to fit, the root mean square error (RMSE) and the mean
absolute percentage error (MAPE) of the trend item subsequence and the periodic
item subsequence are both less than 0.1, and the RMSE is as low as 0.006 for
the periodic item prediction module based on XGBoost\footnote{Accepted in
ICANN2023}.