Displacement Prediction Method for Bank Landslide Based on SSA-VMD and LSTM Model

Xuebin Xie and Yingling Huang ()
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Xuebin Xie: School of Resources and Safety Engineering, Central South University, Changsha 410083, China
Yingling Huang: School of Resources and Safety Engineering, Central South University, Changsha 410083, China

Mathematics, 2024, vol. 12, issue 7, 1-17

Abstract: Landslide displacement prediction is of great significance for the prevention and early warning of slope hazards. In order to enhance the extraction of landslide historical monitoring signals, a landslide displacement prediction method is proposed based on the decomposition of monitoring data before prediction. Firstly, based on the idea of temporal addition, the sparrow search algorithm (SSA) coupled with the variational modal decomposition (VMD) algorithm is used to decompose the total landslide displacement into trend item, periodic item and random item; then, the displacement values of the subitems are fitted by using the long and short-term memory (LSTM) neural network, and the predicted cumulative landslide displacement is obtained by adding up the predicted values of the three subsequences. Finally, the historical measured data of the Shuping landslide is taken as an example. Considering the effects of seasonal rainfall and reservoir water level rise and fall, the displacement of this landslide is predicted, and the prediction results of other traditional models are compared. The results show that the landslide displacement prediction model of SSA-VMD coupled with LSTM can predict landslide displacement more accurately and capture the characteristics of historical signals, which can be used as a reference for landslide displacement prediction.

Keywords: landslide displacement prediction; time series; sparrow search algorithm; variational modal decomposition; long and short-term memory neural network; bank landslide (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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