摘要
在综合考虑影响露天边坡稳定性的11类指标后,引入支持向量机理论,通过统计各类模型误判样本个数,建立了露天边坡稳定性等级判别的最优模型。通过借助R语言实现了分层随机抽样的技术,保证了训练集与测试集样本数据的随机性和差异性。研究表明:基于SVM理论的露天边坡稳定性分级预测模型,可靠性强、预测准确率高。
After considering the 11 factors of influencing the slope stability,the company introduces support vector machine theory by counting the number of misjudgment samples,and establishes the optimal model.By using R language,the company realizes the stratified random sampling technology,and guarantees the randomness and diversity of training set and test set.The research shows that the prediction model of slope stability based on SVM theory has strong reliability and high prediction accuracy.
作者
肖敏
王小天
韩路朋
XIAO Min;WANG Xiaotian;HAN lupeng(Zhejiang Jin'an Design Research Co.,Ltd.,Suichang 323300,China)
出处
《露天采矿技术》
CAS
2018年第1期38-42,共5页
Opencast Mining Technology
关键词
边坡稳定性
预测模型
支持向量机
R语言
分层随机抽样
slope stability
prediction model
support vector machine
R language
stratified random sample
