LANDSLIDE SUSCEPTIBILITY ASSESSMENT FOR ENSHI, HUBEI PROVINCE: With GIS-based certainty factor and certainty factor-logistic regression coupling model

YU Xiao-man; JIAN Wen-xing; SHAO Shan; WANG Hao; JIAN Zhi-hua; WU Kai-feng; ZHANG Feng

doi:10.13686/j.cnki.dzyzy.2024.05.012

2024 Vol. 33, No. 5

Article Contents

Article navigation > Geology and Resources > 2024 > 33(5): 716-724

Next Article Previous Article

YU Xiao-man, JIAN Wen-xing, SHAO Shan, WANG Hao, JIAN Zhi-hua, WU Kai-feng, ZHANG Feng. LANDSLIDE SUSCEPTIBILITY ASSESSMENT FOR ENSHI, HUBEI PROVINCE: With GIS-based certainty factor and certainty factor-logistic regression coupling model[J]. Geology and Resources, 2024, 33(5): 716-724. doi: 10.13686/j.cnki.dzyzy.2024.05.012

Citation:

YU Xiao-man, JIAN Wen-xing, SHAO Shan, WANG Hao, JIAN Zhi-hua, WU Kai-feng, ZHANG Feng. LANDSLIDE SUSCEPTIBILITY ASSESSMENT FOR ENSHI, HUBEI PROVINCE: With GIS-based certainty factor and certainty factor-logistic regression coupling model[J]. Geology and Resources, 2024, 33(5): 716-724. doi: 10.13686/j.cnki.dzyzy.2024.05.012

LANDSLIDE SUSCEPTIBILITY ASSESSMENT FOR ENSHI, HUBEI PROVINCE: With GIS-based certainty factor and certainty factor-logistic regression coupling model

1.
China University of Geosciences, Wuhan 430074, China
2.
Geological Disaster Survey and Monitoring Center, Hubei Geological Environment Station, Wuhan 430034, China

More Information

Corresponding author: JIAN Wen-xing, wxjian@cug.edu.cn

Received Date: 24 November 2022

Revised Date: 12 December 2022

Publish Date: 25 October 2024

Abstract

Abstract

The geological conditions in Enshi Prefecture of Hubei Province are complex, with a high number of geological disasters, especially landslides. Taking the prefecture as the research area, eight influencing factors are selected, including surface slope, slope type, slope aspect, structure, road, water system, formation lithology and vegetation coverage. Based on the statistical analysis of spatial data on ArcGIS platform, the regional landslide susceptibility is evaluated by using the certainty factor (CF) and certainty factor-logistic regression (CF-LR) coupling model. Then the accuracy of both models is verified by comparing the distribution of disaster sites in the validation set in each zoning area and AUC values. The results show that the susceptibility zoning of the two models are generally consistent, although the accuracy of the coupling model is slightly higher. Based on the calculated values of the combined models, Enshi area is classified into low, medium, high and extra-high susceptible zones in terms of landslide susceptibility level, which can provide support for the prevention and control of geological hazards in the area.
- geological disaster /
- landslide /
- susceptibility assessment /
- certainty factor /
- logistic regression /
- Hubei Province

FullText(HTML)

References

[1]	王毅, 方志策, 牛瑞卿, 等. 基于深度学习的滑坡灾害易发性分析[J]. 地球信息科学学报, 2021, 23(12): 2244-2260. doi: 10.12082/dqxxkx.2021.210057 CrossRef Google Scholar Wang Y, Fang Z C, Niu R Q, et al. Landslide susceptibility analysis based on deep learning[J]. Journal of Geo-Information Science, 2021, 23(12): 2244-2260. doi: 10.12082/dqxxkx.2021.210057 CrossRef Google Scholar
[2]	许冲, 戴福初, 姚鑫, 等. 基于GIS与确定性系数分析方法的汶川地震滑坡易发性评价[J]. 工程地质学报, 2010, 18(1): 15-26. Google Scholar Xu C, Dai F C, Yao X, et al. GIS platform and certainty factor analysis method based Wenchuan earthquake-induced landslide susceptibility evaluation[J]. Journal of Engineering Geology, 2010, 18(1): 15-26. Google Scholar
[3]	张俊, 殷坤龙, 王佳佳, 等. 三峡库区万州区滑坡灾害易发性评价研究[J]. 岩石力学与工程学报, 2016, 35(2): 284-296. Google Scholar Zhang J, Yin K L, Wang J J, et al. Evaluation of landslide susceptibility for Wanzhou district of Three Gorges Reservoir[J]. Chinese Journal of Rock Mechanics and Engineering, 2016, 35(2): 284-296. Google Scholar
[4]	吴润泽, 胡旭东, 梅红波, 等. 基于随机森林的滑坡空间易发性评价: 以三峡库区湖北段为例[J]. 地球科学, 2021, 46(1): 321-330. Google Scholar Wu R Z, Hu X D, Mei H B, et al. Spatial susceptibility assessment of landslides based on random forest: A case study from Hubei section in the Three Gorges Reservoir area[J]. Earth Science, 2021, 46(1): 321-330. Google Scholar
[5]	牛瑞卿, 彭令, 叶润青, 等. 基于粗糙集的支持向量机滑坡易发性评价[J]. 吉林大学学报(地球科学版), 2012, 42(2): 430-439. Google Scholar Niu R Q, Peng L, Ye R Q, et al. Landslide susceptibility assessment based on rough sets and support vector machine[J]. Journal of Jilin University (Earth Science Edition), 2012, 42(2): 430-439. Google Scholar
[6]	黄发明, 殷坤龙, 蒋水华, 等. 基于聚类分析和支持向量机的滑坡易发性评价[J]. 岩石力学与工程学报, 2018, 37(1): 156-167. Google Scholar Huang F M, Yin K L, Jiang S H, et al. Landslide susceptibility assessment based on clustering analysis and support vector machine [J]. Chinese Journal of Rock Mechanics and Engineering, 2018, 37(1): 156-167. Google Scholar
[7]	罗路广, 裴向军, 黄润秋, 等. GIS支持下CF与Logistic回归模型耦合的九寨沟景区滑坡易发性评价[J]. 工程地质学报, 2021, 29(2): 526-535. Google Scholar Luo L G, Pei X J, Huang R Q, et al. Landslide susceptibility assessment in Jiuzhaigou scenic area with GIS based on certainty factor and logistic regression model[J]. Journal of Engineering Geology, 2021, 29(2): 526-535. Google Scholar
[8]	Shortliffe E H, Buchanan B G. A model of inexact reasoning in medicine[J]. Mathematical Biosciences, 1975, 23(3/4): 351-379. Google Scholar
[9]	栗泽桐, 王涛, 周杨, 等. 基于信息量、逻辑回归及其耦合模型的滑坡易发性评估研究: 以青海沙塘川流域为例[J]. 现代地质, 2019, 33(1): 235-245. Google Scholar Li Z T, Wang T, Zhou Y, et al. Landslide susceptibility assessment based on information value model, logistic regression model and their integrated model: A case in Shatang River Basin, Qinghai Province [J]. Geoscience, 2019, 33(1): 235-245. Google Scholar
[10]	Yin K L, Yan T Z. Statistical prediction models for slope instability of metamorphosed rocks[C] //Proceedings of the 5th International Symposium on Landslides. Lausanne, 1988. Google Scholar
[11]	Zhao L R, Wu X L, Niu R Q, et al. Using the rotation and random forest models of ensemble learning to predict landslide susceptibility [J]. Geomatics, Natural Hazards and Risk, 2010, 11(1): 1542-1564. Google Scholar
[12]	焦伟之, 张明, 谢鑫鹏, 等. 基于GIS与加权信息量模型的城镇地质灾害易发性评价——以大新镇为例[J]. 安全与环境工程, 2022, 29(4): 119-128. Google Scholar Jiao W Z, Zhang M, Xie X P, et al. Susceptibility evaluation of urban geological disaster based on GIS and weighted information value model: A case study of Daxin Town[J]. Safety and Environmental Engineering, 2022, 29(4): 119-128. Google Scholar
[13]	胡涛, 樊鑫, 王硕, 等. 基于逻辑回归模型和3S技术的思南县滑坡易发性评价[J]. 地质科技通报, 2020, 39(2): 113-121. Google Scholar Hu T, Fan X, Wang S, et al. Landslide susceptibility evaluation of Sinan County using logistics regression model and 3S technology[J]. Bulletin of Geological Science and Technology, 2020, 39(2): 113-121. Google Scholar
[14]	Chen L X, van Westen C J, Hussin H, et al. Integrating expert opinion with modeling for quantitative multi-hazard risk assessment in the Eastern Italian Alps[J]. Geomorphology, 2016, 273: 150-167. Google Scholar
[15]	霍艾迪, 张骏, 卢玉东, 等. 地质灾害易发性评价单元划分方法——以陕西省黄陵县为例[J]. 吉林大学学报(地球科学版), 2011, 41(2): 523-528, 535. Google Scholar Huo A D, Zhang J, Lu Y D, et al. Method of classification for susceptibility evaluation unit for geological hazards: A case study of Huangling County, Shaanxi, China[J]. Journal of Jilin University (Earth Science Edition), 2011, 41(2): 523-528, 535. Google Scholar
[16]	解明礼, 巨能攀, 赵建军, 等. 区域地质灾害易发性分级方法对比分析研究[J]. 武汉大学学报(信息科学版), 2021, 46(7): 1003-1014. Google Scholar Xie M L, Ju N P, Zhao J J, et al. Comparative analysis on classification methods of geological disaster susceptibility assessment[J]. Geomatics and Information Science of Wuhan University, 2021, 46(7): 1003-1014. Google Scholar
[17]	Zhang Y E, Li D Y, Yin K L, et al. The July 1, 2017 Wangjiawan landslide in Ningxiang County, China[J]. Landslides, 2018, 15(8): 1657-1662. Google Scholar
[18]	刘月, 王宁涛, 周超, 等. 基于ROC曲线与确定性系数法集成模型的三峡库区奉节县滑坡易发性评价[J]. 安全与环境工程, 2020, 27(4): 61-70. Google Scholar Liu Y, Wang N T, Zhou C, et al. Evaluation of landslide susceptibility based on ROC and certainty factor method in Fengjie County, Three Gorges Reservoir[J]. Safety and Environmental Engineering, 2020, 27(4): 61-70. Google Scholar
[19]	樊芷吟, 苟晓峰, 秦明月, 等. 基于信息量模型与Logistic回归模型耦合的地质灾害易发性评价[J]. 工程地质学报, 2018, 26(2): 340-347. Google Scholar Fan Z Y, Gou X F, Qin M Y, et al. Information and logistic regression models based coupling analysis for susceptibility of geological hazards [J]. Journal of Engineering Geology, 2018, 26(2): 340-347. Google Scholar
[20]	Chen L X, Guo Z Z, Yin K L, et al. The influence of land use and land cover change on landslide susceptibility: A case study in Zhushan Town, Xuan'en County (Hubei, China)[J]. Natural Hazards and Earth System Sciences, 2019, 19(10): 2207-2228. Google Scholar