生态与农村环境学报 ›› 2015, Vol. 31 ›› Issue (6): 955-962.doi: 10.11934/j.issn.1673-4831.2015.06.024

• 研究方法 • 上一篇    下一篇

基于“3S”技术和LR-WSVM模型的广州市花都区滑坡灾害风险评估与区划研究

张春慧,陈美招,郑荣宝   

  1. 广东工业大学管理学院
  • 收稿日期:2015-03-18 修回日期:2015-09-11 出版日期:2015-11-25 发布日期:2015-11-26
  • 通讯作者: 郑荣宝 E-mail:zhengrongbao@163.com
  • 作者简介:张春慧(1973—),女,内蒙古包头人,讲师,博士,主要研究方向为旅游地质与旅游安全评价。E-mail: zhangchh@gdut.edu.cn
  • 基金资助:

    国家自然科学基金(41201542);教育部人文社科基金(12YJCZH271)

Landslide Hazard Risk Assessment and Zoning of Huadu District of Guangzhou Based on “3S” Technique and Logistic RegressWeighted SVM Model.

ZHANG Chun-hui,CHEN Mei-zhao,ZHENG Rong-bao   

  1. College of Management, Guangdong University of Technology
  • Received:2015-03-18 Revised:2015-09-11 Online:2015-11-25 Published:2015-11-26

摘要:

进行滑坡的风险评估与区划研究,能够为决策者科学制定防灾减灾政策提供帮助。在外业调查及相关研究基础上,选择地形、岩性、植被、土地利用、降水、断裂带和人类活动等11个因子作为评价指标,采用Logistic回归-加权支持向量机模型进行滑坡的风险评价,划分5种风险等级类型,最后采取ROC曲线进行模型精度检验。结果表明,花都区梯面镇大部分区域、花东镇和赤坭镇部分地区是滑坡灾害风险较高的区域,评价结果与65个滑坡灾害存量数据空间分布相吻合;风险等级中很低、较低、中等、较高和很高区域面积比例分别为28.19%、31.31%、25.54%、11.73%和3.24%;受试者工作特证(ROC)曲线的精度验证表明,Logistic回归-加权支持向量机模型能有效进行该区域的滑坡风险评价,且具有较好的评价精度、分类能力和客观性。

关键词: 滑坡灾害, 风险评价, 逻辑回归, 支持向量机, 花都区

Abstract:

Landslide is one of the three major natural hazards in China. It is, therefore, very important to study how to perform landslide risk assessment and zoning. The related studies may provide policymakers with theoretic basis in formulating strategies and policies for control of landslides. On the basis of the field surveys and relevant researches already done, 11 factors, such as terrain, lithology, vegetation, land use, precipitation, fault, human activities, etc. have beer used as evaluation indexes in performing landslide risk assessment for Huadu District with the aid of the logistic regression-weighted support vector machine model. Landslide risks in the region were sorted into 5 grades, and in the end the model in fitting accuracy with ROC curve has been verified. Results show that the risk was quite high in a large portion of Timian Town, and a certain portion of Huadong and Chini towns, which is spatially consistent with distribution of the 65 landslide disaster inventory data; The regions, very low, low, moderate, high and very high in risk accounted for 28.19%, 31.31%, 25.54%,1.73% and 3.24%, respectively, of the district in area. Verification of fitting accuracy with ROC curve shows that the model of logistic regression-weighted SVM may be used effectively to assess landslide risks in this region, with fairly high assessment accuracy, grading ability and objectivity.

Key words: landslide hazard, risk assessment, logistic regression, support vector machine, Huadu District