Abstract
In recent decades, landslide displacement forecasting has received increasing attention due to its ability to reduce landslide hazards. To improve the forecast accuracy of landslide displacement, a dynamic forecasting model based on variational mode decomposition (VMD) and a stack long short-term memory network (SLSTM) is proposed. VMD is used to decompose landslide displacement into different displacement subsequences, and the SLSTM network is used to forecast each displacement subsequence. Then, the forecast values of landslide displacement are obtained by reconstructing the forecast values of all displacement subsequences. On the other hand, the SLSTM networks are updated by adding the forecast values into the training set, realizing the dynamic displacement forecasting. The proposed model was verified on the Dashuitian landslide in China. The results show that compared with the two advanced forecasting models, long short-term memory (LSTM) network, and empirical mode decomposition (EMD)–LSTM network, the proposed model has higher forecast accuracy.
Highlights
Landslides, as one of the most widespread and frequent natural hazards all over the world, directly threaten human life and cause tremendous damage to the human living environment, resources, and property
The results show that compared with the two advanced forecasting models, long short-term memory (LSTM) network, and empirical mode decomposition (EMD)–LSTM network, the proposed model has higher forecast accuracy
To improve the forecast accuracy of landslide displacement, a dynamic forecasting model based on variational mode decomposition (VMD) and a stack long short-term memory network (SLSTM) is proposed
Summary
Landslides, as one of the most widespread and frequent natural hazards all over the world, directly threaten human life and cause tremendous damage to the human living environment, resources, and property. One of the most widely used ideas for landslide displacement forecasting is to decompose the original landslide displacement, and forecast each subsequence separately, and reconstruct all forecast values to obtain the forecast results Following this idea, in [10], wavelet analysis is used to decompose the landslide displacement and a particle swarm-optimized support vector machine is used as the forecasting model. To improve the forecast accuracy of landslide displacement, a dynamic forecasting model based on variational mode decomposition (VMD) and a stack long short-term memory network (SLSTM) is proposed. (2) A SLSTM network with “3 + 1” layers is designed to model and forecast each displacement subsequence, which improves the performance of a basic long short-term memory (LSTM) network;.
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