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Landslide prediction based on improved principal component analysis and mixed kernel function least squares support vector regression model

引用

Journal of Mountain Science 2021年第8期18卷 2130-2142页

作者： LI Li-min CHENG Shao-kang WEN Zong-zhou School of Electronics and Information Xi’an Polytechnic UniversityXi’an710048China School of Automation Northwestern Polytechnical UniversityXi’an710072China

Landslide probability prediction plays an important role in understanding landslide information in advance and taking preventive *** factors can influence the occurrence of landslides,which is easy to have a curse of dimensionality and thus lead to reduce prediction *** the generalization ability of the model will also decline sharply when there are only small *** reduce the dimension of calculation and balance the model’s generalization and learning ability,this study proposed a landslide prediction method based on improved principal component analysis(PCA)and mixed kernel function least squares support vector regression(LSSVR)***,the traditional PCA was introduced with the idea of linear discrimination,and the dimensions of initial influencing factors were reduced from 8 to *** improved PCA can not only weight variables but also extract the original ***,combined with global and local kernel function,the mixed kernel function LSSVR model was framed to improve the generalization *** optimization algorithm(WOA)was used to optimize the ***,Root Mean Square Error(RMSE),the sum of squared errors(SSE),Mean Absolute Error(MAE),Mean Absolute Precentage Error(MAPE),and reliability were employed to verify the performance of the *** with radial basis function(RBF)LSSVR model,Elman neural network model,and fuzzy decision model,the proposed method has a smaller ***,the landslide warning level obtained from the landslide probability can also provide references for relevant decision-making departments in emergency response.

关键词： Landslide probability LSSVR Mixed kernel function Improved PCA warning level

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