东北大学学报（自然科学版）

东北大学学报:自然科学版 ›› 2017, Vol. 38 ›› Issue (4): 571-575.DOI: 10.12068/j.issn.1005-3026.2017.04.024

• 资源与土木工程 • 上一篇    下一篇

基于K-PSO聚类算法和熵值法的滑坡敏感性

阮云凯1， 占洁伟1， 陈剑平1， 李严严2   

  1. (1. 吉林大学 建设工程学院， 吉林 长春130026； 2. 中国科学院 地质与地球物理研究所， 北京100029)
  • 收稿日期:2015-04-06 修回日期:2015-04-06 出版日期:2017-04-15 发布日期:2017-04-11
  • 通讯作者: 阮云凯
  • 作者简介:阮云凯(1989-)，男，福建建阳人，吉林大学博士研究生; 陈剑平(1957-)，男，福建南平人，吉林大学教授，博士生导师.
  • 基金资助:
    国家自然科学基金重点资助项目(41330636); 吉林大学研究生创新基金资助项目(2016208).

Landslide Sensitivity Based on K-PSO Clustering Algorithm and Entropy Method

RUAN Yun-kai1， ZHAN Jie-wei1， CHEN Jian-ping1， LI Yan-yan2   

  1. 1. College of Construction Engineering， Jilin University， Changchun 130026， China; 2. Institute of Geology and Geophysics， Chinese Academy of Sciences， Beijing 100029， China.
  • Received:2015-04-06 Revised:2015-04-06 Online:2017-04-15 Published:2017-04-11
  • Contact: CHEN Jian-ping
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摘要： 引入K-PSO聚类算法和熵值法，建立滑坡敏感性分析模型.选取旭龙水电站库区22处典型滑坡作为研究对象，确定8个主要影响因子:岩体结构、斜坡结构、断层距离、变形迹象、坡体高度、平均坡度、诱发地震、淹没比例.利用熵值法确定影响因子权重值分别为0.152，0.178，0.035，0.106，0.106，0.169，0.193和0.061.采用K-PSO算法对滑坡进行敏感性划分，结果表明，该库区22处滑坡有8处为轻度敏感，9处为中度敏感，4处为重度敏感和1处极度敏感.将评价结果与现场实际调查情况对比分析知，22处滑坡的敏感度水平与现场实际发育情况具有较好的一致性，该方法对旭龙水电站库区滑坡敏感性评价具有良好的指导作用.

关键词: 熵值法, 滑坡, K-PSO, 聚类模型, 敏感性

Abstract: The K-PSO clustering algorithm and entropy method were introduced to establish a sensitivity analysis model for landslide. The 22 typical landslides located in Xulong hydropower station reservoir area were investigated. Eight major factors including rock mass structure， slope structure， fault distance， signs of deformation， slope height， average gradient， induced earthquake and submerged ratio were determined for landslide sensitivity analysis. The weights of major factors determined by the entropy method are 0.152， 0.178， 0.035， 0.106， 0.106， 0.169， 0.193， 0.061， respectively. Sensitivity analysis results based on K-PSO clustering algorithm showed that among the 22 landslides， 8 landslides are evaluated as low sensitive， 9 as moderate， 4 as severely sensitive and one as extremly sensitive. Compared with the in-situ observations， the evlauated level of sensitivity of the 22 landslides agree very well with the actual development of the landslides. The proposed K-PSO method is effective for landslide sensitivity analysis in Xulong hydropower station reservoir area.

Key words: entropy method, landslide, K-PSO, clustering model, sensitivity

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