• 地球信息科学理论与方法 •

### 基于信息量模型和数据标准化的滑坡易发性评价

1. 成都理工大学 地质灾害防治与地质环境保护国家重点实验室,成都 610059
• 收稿日期:2017-11-14 修回日期:2018-03-12 出版日期:2018-05-29 发布日期:2018-05-20
• 通讯作者: 周伟 E-mail:1005940340@qq.com;chouvw@163.com
• 作者简介:

作者简介：杨根云(1993-),男,硕士生,主要从事岩土体稳定性及工程环境效应方面的研究。E-mail: 1005940340@qq.com

• 基金资助:
国家自然科学基金项目(41572300)

### Assessment of landslide Susceptibility Based on Information Quantity Model and Data Normalization

YANG Genyun(), ZHOU Wei*(), FANG Jiaoyong

1. State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China
• Received:2017-11-14 Revised:2018-03-12 Online:2018-05-29 Published:2018-05-20
• Contact: ZHOU Wei E-mail:1005940340@qq.com;chouvw@163.com
• Supported by:
National Natural Science Foundation of China, No.41572300.

Abstract:

This paper used an information quantity model and four normalized models to assess earthquake-induced shallow landslide susceptibility in a geographic information system environment. Four normalized models are Min-Max normalization, the zero-mean normalization, the logarithmic logistic normalization and the arc-tangent function normalization. The approach was applied to the Qushan-Leigu area in the Beichuan County, Sichuan Province, where many co-seismic landslides were triggered by the Wenchuan earthquake in May 2008. Seven impact factors, the slope angle, slope aspect, geology, elevation, distance to faults, distance to rivers and distance to roads, were selected as the most important conditioning factors. To assess the shallow landslide susceptibility, a spatial database of conditioning factors and a landslide inventory map were compiled in ArcGIS using data from a topographic map, a geological map and Spot-5 imageries. The information quantity values of the conditioning factors were computed and normalized based on four normalized models. The weighted values of assessment factors were determined using the analytic hierarchy process. Five landslide susceptibility maps were developed. The performance was evaluated using the receiver operating characteristic (ROC) curve. The values of the area under the curve (AUC) for four normalized models and the information quantity model are 0.807, 0.672, 0.592, 0.615 and 0.684, respectively. A comparison of the results of the landslide susceptibility assessment and the landslide inventory indicates that the Min-Max normalization had the highest predictive performance (AUC=0.807). The landslide susceptibility index values were reclassified into five susceptibility classes using the Natural Breaks approach, namely very high, high, medium, low and very low susceptibility. The results show that the very high susceptibility zone obtained using the Min-Max normalization covers about 20% of the study area. Landslides distributed in the very high susceptibility zone are close to faults and rivers. The final landslide susceptibility map has the potential to the regional landslide risk assessment and geohazard mitigation.