Application of static dynamic coupling model optimized by random forest in landslide displacement prediction
This paper took the Shengjibao landslide in Fengjie county,Chongqing as an example.A static machine learning algorithm called the support vector regression(SVR)and a dynamic machine learning algorithm called the long short-term memory neural network(LSTM)were proposed to pre-dict the landslide displacement.Then,the random forest(RF)algorithm was introduced to classify and predict the optimal solution between the SVR model and the LSTM model.Finally,the RF-opti-mized SVR-LSTM landslide displacement prediction model was obtained by assigning weights to the static-dynamic coupling model(SVR-LSTM)based on the probability values of the output from the RF model.The results show that LSTM model has better performance than the SVR model.RF-optimized SVR-LSTM landslide displacement prediction model integrates the advantages of static and dynamic prediction models,and its prediction performance is better than that of the SVR model and the LSTM model,respectively.This study provides an idea of integrating landslide displacement prediction model,which can provide reference for geological disaster prediction in the Three Gorges Reservoir area.
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