| 多分类支持向量机在滑坡稳定性判识中的应用 |
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| 引用本文： | 李秀珍,孔纪名,王成华.多分类支持向量机在滑坡稳定性判识中的应用[J].吉林大学学报(地球科学版),2010,40(3):631-637. |
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| 作者姓名： | 李秀珍 孔纪名 王成华 |
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| 作者单位： | 1.中国科学院 山地灾害与表生过程重点实验室,成都 610041;2.中国科学院 水利部成都山地灾害与环境研究所,成都 610041;3.西南交通大学 土木工程学院,成都 610031 |
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| 基金项目： | 国家自然科学基金，中国科学院山地灾害与地表过程重点实验室开放基金，中国科学院"西部之光"人才培养计划项目 |
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| 摘 要： | 如何准确地判识和评价滑坡的稳定性一直是滑坡研究中的关键问题。基于多分类支持向量机的基本理论，利用三峡库区的37个典型滑坡（27个训练样本，10个测试样本），建立了滑坡稳定性判识的多分类支持向量机模型，并与距离判别分析方法进行了比较。结果表明，SVM模型对测试样本和训练样本的判识准确率均达到100％，而距离判别法对测试样本和训练样本的判识准确率分别为80％和77.8％,前者的判识精度明显优于后者。在此基础上，将SVM模型运用于溪洛渡库区牛滚凼滑坡的稳定性判识中，结果与实际情况吻合较好。
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| 关 键 词： | 多分类支持向量机 滑坡 稳定性判识 判识指标 |
| 收稿时间： | 2009-06-17 |
Application of Multi-Classification Support Vector Machine in the Identifying of Landslide Stability |
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| LI Xiu-zhen,KONG Ji-ming,WANG Cheng-hua.Application of Multi-Classification Support Vector Machine in the Identifying of Landslide Stability[J].Journal of Jilin Unviersity:Earth Science Edition,2010,40(3):631-637. |
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| Authors: | LI Xiu-zhen KONG Ji-ming WANG Cheng-hua |
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| Institution: | 1.Key Laboratory of Mountain Hazards and Surface Processes, Chinese Academy of Sciences, Chengdu 610041, China;2. Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China;
3. Civil Engineering College, Southwestern Jiaotong University, Chengdu 610031, China |
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| Abstract: | How to accurately identify and assess stability of landslides is always a key problem in the study of landslides. Based on multi-classification support vector machine theory, multi-classification support vector machine model for landslides stability evaluation was built, by using 37 typical landslides(27 training samples and 10 testing samples ) in the Three Gorges reservoir areas, and was compared with distance discrimination analysis method. The results indicates that the accuracy rates of the SVM model for testing samples and training samples are up to 100%, while the accuracy rates of the distance discrimination method for testing samples and training samples are separately 80% and 77.8%. The identification precision of the former is obviously better than that of the latter. On this basis, the SVM model was applied in the stability identification of Niugundang landslide in the Xiluodu reservoir areas, and the obtained results was in good agreement with the actual situation. So, SVM method has good applicability and effectivity in the stability discrimination practice of landslides and can provide a new way for the discrimination and evaluation of landslide stability. |
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| Keywords: | multi-classification support vector machine landslides stability discrimination discrimination indexes |
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