Optimization research of support vector machine in slope stability evaluation
PAN Yu
Abstract：Support Vector Machine(SVM)has been widely applied in slope stability assessment.However,cur-rently,scholars have not reached a consensus on the number and interval of slope stability levels.Moreover,the division of training and validation samples remains largely based on empirical knowledge,and the optimal sample size has received less exploration.To address this,we selected five parameters,including slope height,slope angle,bulk density,cohesion,and internal friction angle,as evaluation indicators for slope stability.We conducted numerical simulations using GeoStudio software to obtain 963 sets of data for different combinations of evaluation indicators.The data was then analyzed and trained using MATLAB software to develop an SVM model.The analysis suggested that slope stability levels can be divided into 8 categories,with an average accu-racy rate of 88.39%for the validation samples.Furthermore,as the number of training samples increases,the accuracy of the validation also improves,assuming a fixed total sample quantity.Additionally,in the absence of other partitioning references,it is advisable to have approximately 12 validation samples for each classifica-tion level.
Keywords：support vector machineslopestability evaluationoptimization
Publication Date：2024-06-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 119-124 )