# Landslide Surface Displacement Prediction Based on VSXC-LSTM Algorithm

arXiv: https://arxiv.org/abs/2307.12524 · HTML: https://mlsanity.com/paper/2307.12524/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 22 references that are in the map.
- First posted on arXiv 24 Jul 2023.

## Builds on (most cited first, 2 of 2)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Deep Kalman Filters](https://mlsanity.com/paper/1511.05121/) (2015)

## Built on by (most cited first, 1 of 1)

- [CC-GRMAS: A Multi-Agent Graph Neural System for Spatiotemporal Landslide Risk Assessment in High Mountain Asia](https://mlsanity.com/paper/2510.20875/) (2025)

## Abstract (arXiv)

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 XGBoostfootnoteAccepted in ICANN2023.