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Remote Sensing for Land & Resources    2021, Vol. 33 Issue (2) : 172-181     DOI: 10.6046/gtzyyg.2020203
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Application of information value model based on symmetrical factors classification method in landslide hazard assessment
LING Xiao1(), LIU Jiamei2,3(), WANG Tao2,3, ZHU Yueqin4, YUAN Lingling4, CHEN Yangyang1
1. School of Information Engineering and Technology, China University of Geosciences (Beijing), Beijing, 100083, China
2. Institute of Geomechanics, Chinese Academy of Geological Sciences, Beijing, 100081, China
3. Key Laboratory of Neotectonics Movement and Geohazard, Beijing, 100081, China
4. Development and Research Center, China Geological Survey, Beijing, 100037, China
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Abstract  

The information value model (IVM) is a statistical prediction method derived from information theory, which is widely used in natural hazard risk assessment. The problem as to how to formulate a suitable factor classification method to maximize the advantages of pre- single factor statistical analysis remains a key issue. In order to solve this problem, the authors processed a method of factor classification by combining symmetrical intervals. Statistical knowledge related to normal distribution was referred, the factors was pre-segmented by 1/2 standard deviation, and the intervals were merged symmetrically from outside to inside. After that, factors approximately fitting normal distribution, such as slope angel and topographic wetness index (TWI), were classified based on this method, and IVM was built, which was later used in landslide hazard susceptibility analysis in Wenchuan area. Meanwhile, 5 standard classification methods were selected and tested as comparative experiments for rationality verification, namely equal quantile (EQ) classification method, natural break (NB) classification method, geometric break (GB) classification method and standard deviation (SD) classification method. The results show that the IVM using symmetrical method as factor classification method stands out among the rests. The actual landslide area ratio in the high and extremely high-risk areas in the susceptibility map reached 80.87%, higher than that obtained by other standard classification methods. This proves that the symmetrical classification method performs well.

Keywords information value model (IVM)      landslide triggered by earthquake      landslide susceptibility analysis      factor classification methods      symmetrical classification method     
ZTFLH:  TP79  
Corresponding Authors: LIU Jiamei     E-mail: LinGX0527@163.com;ljm19870918@126.com
Issue Date: 21 July 2021
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Xiao LING
Jiamei LIU
Tao WANG
Yueqin ZHU
Lingling YUAN
Yangyang CHEN
Cite this article:   
Xiao LING,Jiamei LIU,Tao WANG, et al. Application of information value model based on symmetrical factors classification method in landslide hazard assessment[J]. Remote Sensing for Land & Resources, 2021, 33(2): 172-181.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020203     OR     https://www.gtzyyg.com/EN/Y2021/V33/I2/172
Fig.1  Workflow of landslide hazardness analysis in Wenchuan area using information value model
Fig.2  Schematic diagram of the symmetrical classification method (e.g. standard normal distribution)
Fig.3  Original geographical data of study area in Wenchuan
编号 分类 致灾因子 数据源 致灾指示意义
1 地形地貌因子 坡度 DEM(25 m) 坡面“坡角”几何形态,控制坡体稳定性及滑移距离
2 坡向 DEM(25 m) 阳坡阴坡发生滑坡的可能性不同,地震滑坡现出明显的断层逆冲方向效应和背坡效应
3 高程 DEM(25 m) 海拔的高低影响降水量、温度、植被覆盖度,间接影响斜坡稳定程度
4 到断层距离 断层矢量(1∶100万) 地质体的结构面发育程度,控制坡体稳定性
5 地表曲率 DEM(25 m) 地表曲率是关于地形表面扭曲变化程度的定量化描述,可反映地貌的更深层次信息
编号 分类 致灾因子 数据源 致灾指示意义
6 岩性因子 岩性 数字地质图(1∶20万公开版) 斜坡岩土体的物理和强度特征,控制坡体稳定性
7 水文因子 到河流距离 河网矢量(1∶25万) 坡脚侵蚀及坡体水文地质特征,控制坡体稳定性
8 地形湿度指数 DEM(25 m) 可作为地表水环境的定量化描述
9 地震因子 地震烈度 地震烈度矢量 地震动荷载条件
Tab.1  Selection principles and original data resources of hazardness factors
Fig.4  Statistical analysis results between landslide and slope angle
Fig.5  Statistical analysis results between landslide and aspect
Fig.6  Statistical analysis results between landslide and height
Fig.7  Statistical analysis results between landslide and distance to fault
Fig.8  Statistical analysis results between landslide and curvature, curvature ranges from -0.05 to 0.05
岩性分组 代表岩性种类
1 晋宁期、印支期、华力西期闪长岩、花岗岩
中元古代黄水河群岩浆岩及变质岩系、安山岩、片岩、玄武岩等
侏罗系灰褐色-浅灰绿色含砾砂岩、砂砾岩
2 前震旦系石英岩、片岩夹大理岩,灰绿色酸性火山角砾凝灰岩
前震旦系角斑岩、细碧岩、凝灰岩,印支期、二长岩、正长岩
二叠系灰岩,三叠系、泥盆系白云岩等
3 震旦系、寒武系、泥盆系、侏罗系砂岩、粉砂岩、硅质岩夹板岩、粉砂岩长石石英砂岩夹黏土岩等
三叠系、白垩系含砾砂岩、页岩、紫红色泥岩、变质长石石英砂岩夹千枚岩
侏罗系砾岩、砂岩夹泥岩、砾岩夹岩屑砂岩、粉砂质、黏质砂土等
4 志留系、泥盆系、三叠系千枚岩、板岩、细砂岩、粉砂岩夹灰岩
三叠系、侏罗系砂质泥岩夹煤层、石英砂岩夹黏土层、炭质页岩、长石及岩屑砂岩等
泥盆系粉质泥岩、泥岩
5 第四系亚黏土、亚砂土
第四系冲洪积砂砾石、黏土等
Tab.2  Classification result of lithology and examples matching each group
Fig.9  Statistical analysis results between landslide and lithology
Fig.10  Statistical analysis results between landslide and distance to river
Fig.11  Statistical analysis results between landslide and topographic wetness index
Fig.12  Statistical analysis results between landslide and intensity
Fig.13-1  Different classification results of topographic wetness index using 6 factor classification methods
Fig.13-2  Different classification results of topographic wetness index using 6 factor classification methods
Fig.14-1  Different results of Wenchuan landslide hazard map based on 6 factor classification methods
Fig.14-2  Different results of Wenchuan landslide hazard map based on 6 factor classification methods
等级 EI法 EQ法 NB法 GB法 SD法 对称法
极低 0.01 0.01 0.03 0.04 0.05 0.04
较低 1.07 1.88 2.58 4.14 4.46 2.07
中等 19.68 18.49 22.60 27.17 26.67 17.03
较高 60.20 51.01 59.46 63.67 63.40 39.03
极高 19.04 28.60 15.33 4.98 5.41 41.84
Tab.3  Comparison of the proportion of actual landslide area in high-risk and extremely high-risk areas(%)
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