• 研究论文 •

### 一种基于地理环境相似度的滑坡负样本可信度度量方法

1. 1. 南京师范大学虚拟地理环境教育部重点实验室,南京 210023
2. 江苏省地理环境演化国家重点实验室培育建设点,南京 210023
3. 江苏省地理信息资源开发与利用协同创新中心,南京 210023
4. 中国科学院地理科学与资源研究所,资源与环境信息系统国家重点实验室,北京 100101
5. 威斯康辛—麦迪逊大学地理系,美国 麦迪逊 53706
• 出版日期:2016-07-25 发布日期:2016-07-25
• 通讯作者: 朱阿兴 E-mail:axing@njnu.edu.cn
• 基金资助:
基金项目：国家自然科学基金项目(41431177,41471178);江苏省高校自然科学研究重大项目(14KJA170001);国家重点基础研究发展计划(973计划)项目(2015CB954102)

### A method for quantifying the reliability of landslide pseudo-absence samples based on geographic environmental similarity

MIAO Yamin1,2,3,ZHU A-Xing1,2,3,4,5,*(),YANG Lin4,BAI Shibiao1,2,3,LIU Junzhi1,2,3

1. 1. Key Laboratory of Virtual Geographic Environment of Ministry of Education, Nanjing Normal University, Nanjing 210023, China
2. State Key Laboratory Cultivation Base of Geographical Environment Evolution of Jiangsu Province, Nanjing 210023, China
3. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
4. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
5. Department of Geography, University of Wisconsin-Madison, Madison WI 53706, USA
• Online:2016-07-25 Published:2016-07-25
• Contact: ZHU A-Xing E-mail:axing@njnu.edu.cn
• Supported by:
Foundation: National Natural Science Foundation of China, No.41431177, 41471178;Natural Science Foundation of the Higher Education Institutions of Jiangsu Province, No.14KJA170001;National Basic Research Program of China (973 Program), No.2015CB954102

Abstract:

Landslide pseudo-absence samples play an important role in applying statistical methods for landslide susceptibility mapping. It can constrain the overestimation of landslide susceptibility. The reliability of pseudo-absence samples is unknown in existing methods of pseudo-absence sample generation in landslide susceptibility mapping. The absence samples they generate may contain sites very susceptible to landslides but have not yet failed in the past simply because of the lack of triggering factors. These false absence samples lower the quality of absence samples and in turn lower the quality of the entire training sample set, and then decrease the accuracy of the mapping using statistical methods. In this article, a new method of measuring the reliability of pseudo-absence samples is proposed. The basic principle of the method is that the more similar the geographic environment is between two locations, the closer the geographic features are between them. Accordingly, the more similar the geographic environment of a location is with landslide presence samples, the more likely is the location prone to landslides and the less reliable is this location as an absence sample. According to the inference above, the reliability of a grid as an absence sample can be calculated by the similarity of its geographic environment with the typical environmental conditions under which landslides occurred. The Youfang catchment in southern Gansu Province—an area with high risk for landslides—was used as the study area to apply the proposed method and map the reliability of each grid chosen as pseudo-absence sample. The landslide initial zones in the Youfang catchment were used to validate the effectiveness of the proposed method. The results show that the mean reliability of grids in the landslide initial zones chosen as pseudo-absence samples is 0.26 and the reliability of more than 95% of the grids is lower than 0.5. This indicates that the proposed method of measuring the reliability of pseudo-absence samples is effective.