Susceptibility assessment of geological disasters based on an information value model: A case of Gulin County in Southeast Sichuan
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摘要: 地质灾害威胁着山区人民生命财产安全, 进行地质灾害易发性评价有助于山区城镇进行规划与建设时规避灾害风险。以川东南古蔺县为例, 基于ArcGIS空间分析获取了研究区高程、坡度、岩性、斜坡结构、植被指数、距断层距离和距道路距离7个评价因子, 采用信息量模型分别对滑坡和崩塌灾害进行易发性评价后, 进一步利用ArcGIS单元统计功能对比了滑坡和崩塌易发性的信息量值, 选取相对更大的信息量值作为该栅格的最终信息量值, 绘制了研究区综合地质灾害易发性图, 利用自然断点法将古蔺县按信息量值的大小划分为极低、低、中、高和极高易发区。结果表明: 地质灾害主要分布在断层和道路附近, 断层和人类工程活动是造成研究区地质灾害频发的主要原因; 高易发区与极高易发区面积之和为1 315.62 km2, 占全区总面积的41.32%;预测模型性能经ROC曲线检验, AUC值为0.812 5, 说明栅格最大值法预测的古蔺县综合地灾易发性效果良好。Abstract: Geological disasters threaten the safety of people's lives and property in mountainous areas. The assessment of the susceptibility to geological disasters will help mountainous towns carry out planning and construction to avoid disaster risks. Gulin County in Southeast Sichuan was chosen as the study area. First, based on the ArcGIS spatial analysis, we select the seven evaluation factors of elevation, slope, lithology, slope structure, vegetation index, distance from fault and distance from road and use the information value model to assess the susceptibility of landslide and rockfall disasters. Furthermore, the ArcGIS cell statistics function was used to compare the information value of landslide and rockfall susceptibility, and a relatively larger information value was selected as the final information value of the raster unit to draw the comprehensive geological hazard susceptibility map of the study area. The susceptibility of geological disasters in Gulin County is divided into extremely low, low, medium, high and extremely high susceptibility zones by the natural breaks method. The results show that geological disasters are mainly distributed near faults and roads. Faults and human engineering activities are the main reasons for the frequent occurrence of geological disasters in the study area. The total area of high and extremely high susceptibility zones is 1 315.62 km2, accounting for 41.32% of the total area. The performance of the prediction model is tested by the ROC curve, and the AUC is 0.812 5, indicating that the comprehensive disaster susceptibility of Gulin County predicted by the raster maximum method is good.
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图 2 综合地质灾害易发性评价建模流程图
Figure 2. Modelling flow chart of the comprehensive geohazard susceptibility assessment
图 3 滑坡易发性评价因子分级图
Figure 3. Classification maps of landslide susceptibility assessment factors
图 4 崩塌易发性评价因子分级图
Figure 4. Classification maps of rockfall susceptibility assessment factors
图 5 因子分级内信息量值及滑坡灾害分布
图c, e因分级太多只相间列出，具体分级见正文和表 1
Figure 5. Distribution of landslide-disaster and information value within factor classifications
图 6 因子分级内信息量值及崩塌灾害分布
图c, e因分级太多只相间列出，具体分级见正文和表 1
Figure 6. Distribution of rockfall-hazard and information value within factor classifications
图 7 古蔺县滑坡(a)与崩塌(b)灾害易发性分区图
Figure 7. Susceptibility distribution map of landslides (a) and rockfalls (b) in Gulin County
图 8 基于信息量模型的古蔺县地质灾害易发性分区图
Figure 8. Geological hazard susceptibility division diagram of Gulin County based on the information value model
图 9 基于信息量模型的地质灾害易发性评价ROC曲线
Figure 9. ROC curve of geological hazard susceptibility assessment based on information value model
表 1 滑坡和崩塌评价因子分级和信息量值
Table 1. Classification and information value of land slide and rockfall assessment factors
评价因子 分级状态 归一信息量 评价因子 分级状态 归一信息量 滑坡 崩塌 滑坡 崩塌 高程/m < 500 1.00 1.00 坡度/(°) < 5 0.58 0.62 [500~700) 0.89 0.75 [5, 10) 0.96 0.00 [700~850) 0.92 0.77 [10, 15) 0.98 0.65 [850~1 000) 0.86 0.74 [15, 20) 1.00 0.10 [1 000~1 100) 0.68 0.72 [20, 25) 0.96 0.44 [1 100~1 200) 0.29 0.53 [25, 30) 0.89 0.34 [1 200~1 400) 0.00 0.00 [30, 35) 0.72 1.00 [1 400~1 849) 0.61 0.34 [35, 40) 0.29 0.07 岩性 工程岩组1 0.36 0.19 [40, 45) 0.19 0.68 工程岩组2 0.00 0.00 >45 0.00 0.63 工程岩组3 0.71 0.29 距断层距离/km < 2 0.82 0.14 工程岩组4 0.51 0.35 [2, 4) 0.75 0.03 工程岩组5 0.48 0.41 [4, 6) 0.85 0.13 工程岩组6 0.37 0.25 [6, 8) 1.00 0.49 工程岩组7 0.45 0.32 [8, 10) 0.93 0.44 工程岩组8 0.30 0.38 [10, 12) 0.88 1.00 工程岩组9 0.35 0.30 [12, 14) 0.39 0.10 工程岩组10 0.40 0.31 [14, 16) 0.86 0.15 工程岩组11 0.24 0.30 [16, 18) 0.24 0.15 工程岩组12 0.07 0.19 [18, 20) 0.03 0.00 工程岩组13 1.00 1.00 [20, 22) 1.00 0.15 距道路距离/km < 0.5 0.49 1.00 [22, 24) 0.34 0.56 [0.5, 1) 0.29 0.41 [24, 26) 0.78 0.15 [1, 1.5) 0.04 0.33 [26, 28) 0.00 0.83 [1.5, 2) 0.32 0.00 [28, 30) 0.78 0.15 [2, 3) 0.00 0.42 [30, 32) 0.20 0.31 [3, 4) 0.23 0.32 [32, 34) 0.78 0.15 [4, 5) 0.32 0.28 [34, 36) 0.78 0.15 [5, 6) 0.64 0.65 [36, 38) 0.78 0.15 [6, 7) 0.40 0.66 归一化植被指数NDVI < 0 / 0.48 [7, 8) 0.30 0.66 [0, 0.1) / 0.60 [8, 9) 1.00 0.66 [0.1, 0.2) / 0.60 [9, 10) 0.30 0.66 [0.2, 0.3) / 0.49 斜坡结构 顺向坡 0.00 / [0.3, 0.4) / 1.00 斜向坡 1.00 / [0.4, 0.5) / 0.00 横向坡 0.33 / >0.5 / 0.48 逆向坡 1.00 / 
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[1] 谭玉敏, 郭栋, 白冰心, 等. 基于信息量模型的涪陵区地质灾害易发性评价[J]. 地球信息科学学报, 2015, 17(12): 1554-1562. https://www.cnki.com.cn/Article/CJFDTOTAL-DQXX201512020.htmTan Y M, Guo D, Bai B X, et al. Geological hazard risk assessment based on information quantity model in Fuling district, Chongqing City, China[J]. Journal of Geo-Information Science, 2015, 17(12): 1554-1562(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-DQXX201512020.htm [2] 苏凤环, 崔鹏, 张建强, 等. 芦山地震重灾区崩塌滑坡易发性评价[J]. 山地学报, 2013, 31(4): 502-509. doi: 10.3969/j.issn.1008-2786.2013.04.017Su F H, Cui P, Zhang J Q, et al. Rockfall and landside susceptibility assessment in Lushan Earthquake region[J]. Mountain Research, 2013, 31(4): 502-509(in Chinese with English abstract). doi: 10.3969/j.issn.1008-2786.2013.04.017 [3] 任敬, 范宣梅, 赵程, 等. 贵州省都匀市滑坡易发性评价研究[J]. 水文地质工程地质, 2018, 45(5): 171-178. https://www.cnki.com.cn/Article/CJFDTOTAL-SWDG201805024.htmRen J, Fan X M, Zhao C, et al. Evaluation of the landslide vulnerability in Duyun of Guizhou Province[J]. Hydrogeology and Engineering Geology, 2018, 45(5): 171-178(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-SWDG201805024.htm [4] 孟庆华, 孙炜锋, 王涛. 陕西凤县地质灾害易发性评价研究[J]. 工程地质学报, 2011, 19(3): 388-396. doi: 10.3969/j.issn.1004-9665.2011.03.015Meng Q H, Sun W F, Wang T. GIS based four zones of geohazard susceptibility degrees for Fengxian County in Qinling Mountains of Shaanxi Province[J]. Journal of Engineering Geology, 2011, 19(3): 388-396(in Chinese with English abstract). doi: 10.3969/j.issn.1004-9665.2011.03.015 [5] 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): 15-21. doi: 10.3969/j.issn.1004-9665.2010.01.003Xu 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): 15-21(in Chinese with English abstract). doi: 10.3969/j.issn.1004-9665.2010.01.003 [6] 胡燕, 李德营, 孟颂颂, 等. 基于证据权法的巴东县城滑坡灾害易发性评价[J]. 地质科技通报, 2020, 39(3): 187-194. https://www.cnki.com.cn/Article/CJFDTOTAL-DZKQ202003023.htmHu Y, Li D Y, Meng S S, et al. Landslide susceptibility evaluation in Badong County based on weights of evidence method[J]. Bulletin of Geological Science and Technology, 2020, 39(3): 187-194(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-DZKQ202003023.htm [7] Komac M. A landslide susceptibility model using the analytical hierarchy process method and multivariate statistics in perialpine Slovenia[J]. Geomorphology, 2006, 74(1): 17-28. [8] 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. https://www.cnki.com.cn/Article/CJFDTOTAL-DZKQ202002013.htmHu 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(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-DZKQ202002013.htm [9] 黄发明, 汪洋, 董志良, 等. 基于灰色关联度模型的区域滑坡敏感性评价[J]. 地球科学, 2019, 44(2): 664-676. https://www.cnki.com.cn/Article/CJFDTOTAL-DQKX201902027.htmHuang F M, Wang Y, Dong Z L, et al. Regional landslide susceptibility mapping based on grey relational degree model[J]. Earth Science, 2019, 44(2): 664-676(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-DQKX201902027.htm [10] 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. https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX201801016.htmHaung 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(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX201801016.htm [11] Zhu L, Huang L, Fan L, et al. Landslide susceptibility prediction modeling based on remote sensing and a novel deep learning algorithm of a cascade-parallel recurrent neural network[J]. Sensors, 2020, 20(6): 1576. doi: 10.3390/s20061576 [12] 黄发明, 曹中山, 姚池, 等. 基于决策树和有效降雨强度的滑坡危险性预警[J]. 浙江大学学报: 工学版, 2021, 55(3): 472-482. https://www.cnki.com.cn/Article/CJFDTOTAL-ZDZC202103007.htmHuang F M, Cao Z S, Yao C, et al. Landslides hazard warning based on decision tree and effective rainfall intensity[J]. Journal of Zhejiang University: Engineering Science, 2021, 55(3): 472-482(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-ZDZC202103007.htm [13] Zhao L, Wu X, Niu R, et al. Using the rotation and random forest models of ensemble learning to predict landslide susceptibility[J]. Geomatics, Natural Hazards and Risk, 2020, 11(1): 1542-1564. doi: 10.1080/19475705.2020.1803421 [14] 张以晨, 秦胜伍, 翟健健, 等. 基于信息量的长白山地区泥石流易发性评价[J]. 水文地质工程地质, 2018, 45(2): 150-158. https://www.cnki.com.cn/Article/CJFDTOTAL-SWDG201802023.htmZhang Y C, Qin S W, Zhai J J, et al. Susceptibility assessment of debris flow based on GIS and weight information for the Changbai Mountain area[J]. Hydrogeology & Engineering Geology, 2018, 45(2): 150-158(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-SWDG201802023.htm [15] Pradhan B. A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS[J]. Computers & Geosciences, 2013, 51(2): 350-365. [16] Pham B T, Pradhan B, Bui D T, et al. A comparative study of different machine learning methods for landslide susceptibility assessment: A case study of Uttarakhand area(India)[J]. Environmental Modelling & Software, 2016, 84: 240-250. [17] 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. https://www.cnki.com.cn/Article/CJFDTOTAL-WHCH202107005.htmXie 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(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-WHCH202107005.htm [18] 连志鹏, 徐勇, 付圣, 等. 采用多模型融合方法评价滑坡灾害易发性: 以湖北省五峰县为例[J]. 地质科技通报, 2020, 39(3): 178-186. https://www.cnki.com.cn/Article/CJFDTOTAL-DZKQ202003022.htmLian Z P, Xu Y, Fu S, et al. Landslide susceptibility assessment based on multi-model fusion method: A case study in Wufeng County, Hubei Province[J]. Bulletin of Geological Science and Technology, 2020, 39(3): 178-186(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-DZKQ202003022.htm [19] 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. https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX201404018.htmZhang 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(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX201404018.htm [20] Liao L P, Zhu Y Y, Zhao Y L, et al. Landslide integrated characteristics and susceptibility assessment in Rongxian County of Guangxi, China[J]. Journal of Mountain Science, 2019, 16(3): 657-676. doi: 10.1007/s11629-017-4804-2 [21] 王雷, 吴君平, 赵冰雪, 等. 基于GIS和信息量模型的安徽池州地质灾害易发性评价[J]. 中国地质灾害与防治学报, 2020, 31(3): 96-103. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDH202003017.htmWang L, Wu J P, Zhao B X, et al. Susceptibility assessment of geohazards in Chizhou City of Anhui Province based on GIS and informative model[J]. The Chinese Journal of Geological Hazard and Control, 2020, 31(3): 96-103(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDH202003017.htm [22] 唐川, 马国超. 基于地貌单元的小区域地质灾害易发性分区方法研究[J]. 地理科学, 2015, 35(1): 91-98. https://www.cnki.com.cn/Article/CJFDTOTAL-DLKX201501011.htmTang C, Ma G C. Small regional geohazards susceptibility mapping based on geomorphic unit[J]. Scientia Geographica Sinica, 2015, 35(1): 91-98(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-DLKX201501011.htm [23] 王宁涛, 彭轲, 黎清华, 等. 基于RS和GIS的地质灾害易发性定量评价: 以湖北省五峰县为例[J]. 地学前缘, 2012, 19(6): 221-229. https://www.cnki.com.cn/Article/CJFDTOTAL-DXQY201206028.htmWang N T, Peng K, Li Q H, et al. Quantitative evaluation of geological disaster liability based on RS & GIS analysis: A case study of Wufeng County, Hubei Province[J]. Earth Science Frontiers, 2012, 19(6): 221-229(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-DXQY201206028.htm [24] 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. https://www.cnki.com.cn/Article/CJFDTOTAL-GCDZ202102022.htmLuo 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(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-GCDZ202102022.htm [25] 李文彬, 范宣梅, 黄发明, 等. 不同环境因子联接方法和数据驱动模型对滑坡易发性预测建模的影响规律[J/OL]. 地球科学: 1-20[2021-12-18]. doi: 10.3799/dqkx.2021.042Li W B, Fan X M, Huang F M, et al. Influence law of different environmental factor connection methods and data-based models on landslide susceptibility prediction modeling[J]. Earth Scienc: 1-20[2021-12-18]. doi: 10.3799/dqkx.2021.042. [26] Sciarra M, Coco L, Urbano T. Assessment and validation of GIS-based landslide susceptibility maps: A case study from Feltrino stream basin(Central Italy)[J]. Bulletin of Engineering Geology and the Environment, 2017, 76(2): 437-456. doi: 10.1007/s10064-016-0954-7 [27] 韩用顺, 梁川, 崔鹏, 等. 地形条件对次生山地质灾害害易发性分析[J]. 四川大学学报: 工程科学版, 2010, 42(增刊1): 15-21.Han Y S, Liang C, Cui P, et al. Susceptibility of mountain hazards triggered by Wenchuan Earthquake to topographic factors[J]. Journal of Sichuan University: Engineering Science Edition, 2010, 42(S1): 15-21(in Chinese with English abstract). [28] 张超, 陈艳, 张宇飞, 等. 基于多元线性回归模型的云南昭通地质灾害易发性评价[J]. 水文地质工程地质, 2016, 43(3): 159-163. https://www.cnki.com.cn/Article/CJFDTOTAL-SWDG201603026.htmZhang C, Chen Y, Zhang Y F, et al. Geohazard susceptibility evaluation in Zhaotong of Yunnan based on the multivariate linear regression mode[J]. Hydrogeology and Engineering Geology, 2016, 43(3): 159-163(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-SWDG201603026.htm [29] 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. https://www.cnki.com.cn/Article/CJFDTOTAL-GCDZ201802008.htmFan 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(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-GCDZ201802008.htm [30] 李文彦, 王喜乐. 频率比与信息量模型在黄土沟壑区滑坡易发性评价中的应用与比较[J]. 自然灾害学报, 2020, 29(4): 213-220. https://www.cnki.com.cn/Article/CJFDTOTAL-ZRZH202004022.htmLi W Y, Wang X L. Application and comparison of frequency ratio and information value model for evaluating landslide susceptibility of loess gully region[J]. Journal of Natural Disasters, 2020, 29(4): 213-220(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-ZRZH202004022.htm [31] 赵良军, 陈冬花, 李虎, 等. 基于二元逻辑回归模型的新疆果子沟滑坡风险区划[J]. 山地学报, 2017, 35(2): 203-211. https://www.cnki.com.cn/Article/CJFDTOTAL-SDYA201702010.htmZhao L J, Chen D H, Li H, et al. A method to assess landslide susceptibility by using logistic regression model for Guozigou region, Xinjinag[J]. Mountain Research, 2017, 35(2): 203-211(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-SDYA201702010.htm [32] 孟祥瑞, 裴向军, 刘清华, 等. GIS支持下基于因子分析法的都汶路沿线地质灾害易发性评价[J]. 中国地质灾害与防治学报, 2016, 27(3): 106-115. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDH201603016.htmMeng X R, Pei X J, Liu Q H, et al. GIS-based susceptibility assessment of geological hazards along the road from Dujiangyan to Wenchuan by factor analysis[J]. The Chinese Journal of Geological Hazard and Control, 2016, 27(3): 106-115(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDH201603016.htm [33] 孙长明, 马润勇, 尚合欣, 等. 基于滑坡分类的西宁市滑坡易发性评价[J]. 水文地质工程地质, 2020, 47(3): 173-181. https://www.cnki.com.cn/Article/CJFDTOTAL-SWDG202003022.htmSun C M, Ma R Y, Shang H X, et al. Landslide susceptibility assessment in Xining based on landslide classification[J]. Hydrogeology & Engineering Geology, 2020, 47(3): 173-181(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-SWDG202003022.htm [34] 张玘恺, 凌斯祥, 李晓宁, 等. 九寨沟县滑坡灾害易发性快速评估模型对比研究[J]. 岩石力学与工程学报, 2020, 39(8): 1595-1610. https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX202008009.htmZhang Q K, Ling S X, Li X N, et al. Comparison of landslide susceptibility mapping rapid assessment models in Jiuzhaigou County, Sichuan Province, China[J]. Chinese Journal of Rock Mechanics and Engineering, 2020, 39(8): 1595-1610(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX202008009.htm -
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