基于SOCP-MKRVM的区域滑坡敏感性分析研究  

Study on regional landslide susceptibility based on SOCP-MKRVM

作　　者：林永良[1,2] 夏克文[1] 王志恒[2] 姜晓庆[1] 

机构地区：[1]河北工业大学电子信息工程学院,天津300401 [2]天津城建大学计算中心,天津300384

出　　处：《中国安全生产科学技术》2016年第12期64-68,共5页Journal of Safety Science and Technology

基　　金：河北省自然科学基金(E2016202341);河北省高等学校科学技术研究项目(BJ2014013)

摘　　要：为了提高相关向量机(RVM)在区域滑坡敏感性评价中的预测能力,提出了基于二阶锥规划的多核相关向量机(SOCP-MKRVM)预测模型。以四川省低山丘陵区为例,选取了8个滑坡孕灾因子训练RVM预测模型,并分别运用受试者工作特征曲线(ROC)和滑坡点密度2种方法对预测结果进行验证。通过与单核RVM模型的对比分析,结果表明:SOCP-MKRVM模型提高了对区域滑坡敏感性的评价能力,预测精度提高到71.33%,ROC曲线下面积达到0.741,滑坡点密度分布更加合理,两低敏感区之和为0.89个/100 km^2,两高敏感区之和为6.54个/100 km^2。In order to improve the prediction ability of relevance vector machine（ RVM） for the regional landslide susceptibility assessment,a prediction model of multiple-kernel RVM based on second-order cone programming（ SOCP-MKRVM）was proposed. Taking the low hilly area of Sichuan Province as example,eight landslide-predisposing factors were selected to train the RVM prediction model,and two methods which include the receiver-operating characteristic curve（ ROC） and the landslides dot density were used to verify the prediction results of the model. Through the contrastive analysis with the single kernel RVM model,the results showed that the SOCP-MKRVM model improved the assessment ability of the regional landslide susceptibility. The prediction accuracy increased to 71. 33%,the area under the ROC curve reached 0. 741,and the distribution of landslide dot density was more reasonable,with the sum of two low susceptibility areas as 0. 89 /100 km2 and the sum of two high susceptibility area as 6. 54 /100 km2.

关 键 词：相关向量机 二阶锥规划 滑坡敏感性 ROC 滑坡点密度 

分 类 号：X935[环境科学与工程—安全科学]