常州大学学报（自然科学版）2024，Vol.36Issue(3)：80-92,13.DOI:10.3969/j.issn.2095-0411.2024.03.009
随机森林优化的静动态耦合模型在滑坡位移预测中的应用
Application of static dynamic coupling model optimized by random forest in landslide displacement prediction
摘要
Abstract
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.关键词
滑坡位移预测/随机森林/长短期记忆神经网络/支持向量回归/算法集成Key words
landslide displacement prediction/random forest/long short-term memory neural net-work/support vector regression/algorithm integrated分类
资源环境引用本文复制引用
蒋宏伟,刘健鹏,王新杰,陈春红,刘惠..随机森林优化的静动态耦合模型在滑坡位移预测中的应用[J].常州大学学报（自然科学版）,2024,36(3):80-92,13.基金项目
常州大学人才引进资助项目(ZMF22020036). （ZMF22020036）
