Landslide Surface Displacement Prediction Based on VSXC-LSTM Algorithm

TL;DR

This paper introduces VSXC-LSTM, a novel time series prediction framework combining variational mode decomposition and advanced machine learning techniques, to improve the accuracy of landslide surface displacement forecasts.

Contribution

The paper presents a new hybrid model, VSXC-LSTM, that enhances landslide displacement prediction accuracy by integrating variational mode decomposition with LSTM and XGBoost methods.

Findings

Model achieves RMSE and MAPE below 0.1 for key subsequences.

Periodic item prediction RMSE as low as 0.006.

Model performs well on real landslide data.

Abstract

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}.