Evaluation method of geological hazard susceptibility: A case study on GIS and CF-Logistic regression model in Huangzhong, Qinghai
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摘要:
研究地质灾害易发性的评价方法，对地质灾害防治有着非常重要的现实意义。本文以青海省西宁市湟中县为研究区域，选取高程、坡度、坡向、地形起伏度、距河流距离、距断层距离和工程岩组7个评价因子，利用确定性系数与逻辑回归模型相结合的方法计算出每个单元格地质灾害发生的概率。同时利用ROC曲线和AUC值对模型分类精度进行验证，最终得到AUC值为0.863，说明该方法对湟中县地质灾害易发性评价具有较强的适用性和客观性。本文研究表明，高层、坡向、距河流距离和工程岩组4个因子对研究区地质灾害的影响最为显著。从地质灾害的空间分布来看，该方法计算结果表明极高、高易发区主要分布在湟水河及其干流两侧低山丘陵地区，低易发区主要分布在研究区北部和西南地区。从评价因子的角度分析，高易发区主要分布在离河流500 m的松散冲洪积岩层和软弱层状碎屑岩岩层上。以上研究结果表明，基于CF-Logistic回归模型对研究区地质灾害易发性评价有较强的参考价值，能为研究区地质灾害的防治工作提供理论依据及方法。
Abstract:The evaluation method for the susceptibility of geological hazards has very important practical significance for the prevention and control of geological hazards. This paper takes Huangzhong County, Xining City, Qinghai Province as the research area to investigate the most suitable assessment method for the area’s geological hazard susceptibility. The results show that using seven evaluation factors (including elevation, slope, aspect, topographic undulation, distance from river, distance from fault, and engineering rock group) and the certainty factor and logistic regression model to evaluate the geological hazard susceptibility of Huangzhong County is suitable and reliable. Using the determinant coefficient and logistic regression model, we calculate the probability of geological hazard in each cell. Subsequently, we use the ROC curve and the AUC value to verify the classification accuracy of the model. The AUC value is 0.863, indicating that our method has good applicability for evaluating the susceptibility of geological disasters in the study area. The four factors of high-rise, aspect, distance from river, and engineering rock group have the most significant impacts on geological disasters in the study area. From the perspective of the spatial distribution of geological hazard susceptibility, extremely high and high susceptibility areas calculated using this method are mainly distributed in the low mountain and hilly areas on both sides of the Huangshui River and its main stream, while low susceptibility areas are mainly distributed in the northern and southern regions of the study area. From the perspective of geological hazard evaluation factors, high susceptibility areas are mainly distributed on loose alluvial strata and weak clastic rock strata 500 m away from the river.The research results indicate that the CF-Logistic regression model has strong reference value for evaluating the susceptibility of geological hazards in the study area and can provide theoretical and methodological basis for the prevention and control of geological hazards in the study area.
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表 1 各影响因子分类级别的 CF值
Table 1. Values of CF for the seven factors influencing geologic hazard in Huangzhong
评价因子 分类级别 类别面积/km2 灾害点/个 CF 评价因子 分类级别 类别面积/km2 灾害点/个 CF 高程/m 2285 ~2561 409.002 106 0.446449 距河流
距离/m0~500 1336.213 297 0.322411 2562 ~2745 716.86 251 0.640582 500~ 1000 651.169 44 － 0.625782 2746 ~2952 418.673 52 － 0.267671 1000 ~1500 228.218 15 － 0.636713 2953 ~3183 355.947 8 － 0.881270 1500 ~2000 94.841 12 － 0.251972 3184 ~3429 329.91 0 － 1.000000 2000 ~2500 54.777 7 － 0.243409 3430 ~3722 239.063 0 － 1.000000 2500 ~3000 39.933 16 0.710336 3723 ~4467 100.912 0 － 1.000000 3000 ~3500 30.792 7 0.341811 坡度/° 0~5 372.734 33 － 0.498401 3500 ~4000 25.795 3 － 0.320384 5~10 320.274 84 0.455303 4000 ~4500 21.518 2 － 0.470853 10~15 396.794 89 0.330288 > 4500 87.111 14 － 0.011157 15~20 388.188 83 0.287955 距断层
距离/m0~ 2000 550.712 29 － 0.712956 20~25 341.139 66 0.192716 2000 ~4000 359.757 58 － 0.007449 25~30 302.074 30 － 0.430602 4000 ~6000 281.879 70 0.413851 30~35 256.745 20 － 0.563754 6000 ~8000 238.574 63 0.460318 35~40 144.7 11 － 0.575143 8000 ~10000 220.945 44 0.221240 >40 47.719 1 － 0.889468 10000 ~12000 213.104 37 0.078309 坡向/° 平面 2.247 0 － 1.000000 12000 ~14000 211.939 32 − 0.081653 北 357.756 22 － 0.661639 14000 ~16000 192.899 36 0.156013 东北 404.466 36 － 0.495471 > 16000 300.557 48 － 0.018560 东 398.679 80 0.228596 工程岩组 坚硬块状侵入岩岩组 175.234 3 － 0.910053 东南 300.292 106 0.645050 较坚硬块状火山岩岩组 927.608 0 － 1.000000 南 233.271 46 0.211630 西南 244.869 42 0.064628 坚硬层状碎屑岩岩组 98.676 25 0.429305 西 290.841 52 0.110546 软弱层状碎屑岩岩组 674.074 154 0.346023 西北 337.945 33 － 0.441177 地形起
伏度/m平坦起伏 789.873 135 0.060620 较坚硬层状碎屑岩岩组 18.65 0 － 1.000000 小起伏 899.687 196 0.304749 松散层状冲洪积岩岩组 845.456 225 0.465994 中起伏 623.485 70 － 0.346908 坚硬-较坚硬层状
及块状变质岩岩组730.669 10 － 0.928345 山地起伏 257.322 16 － 0.657623 
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表 2 影响因子间的相关系数矩阵
Table 2. Correlation matrix of seven factors
因子 高程 坡度 坡向 地形起伏度 距河流距离 距断层距离 工程岩组 高程 1 0.241 0.105 0.311 0.134 0.115 0.182 坡度 1 －0.024 0.693 0.052 0.132 0.296 坡向 1 －0.024 0.077 0.137 0.101 地形起伏度 1 0.036 0.153 0.390 距河流距离 1 0.057 0.124 距断层距离 1 0.225 工程岩组 1
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表 3 逻辑回归分析结果
Table 3. Results of logistic regression analysis
回归项 B SE wals df Sig 高程 1.993 0.229 75.665 1 0.000 坡度 1.283 0.305 17.641 1 0.000 坡向 1.338 0.224 35.719 1 0.000 距河流距离 1.539 0.229 45.258 1 0.000 距断层距离 0.281 0.294 0.914 1 0.000 工程岩组 0.858 0.303 8.008 1 0.005 常量 －0.663 0.126 27.818 1 0.000
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表 4 地质灾害易发性分区统计表
Table 4. Results of geological hazard susceptibility in different districts
易发性分级 面积/km2 占研究区面积比例/% 灾害点个数/个 占灾害点总数比例/% 地灾密度 极低易发区 516.611 20.099 3 0.719 0.006 低易发区 589.298 22.927 6 1.439 0.010 中易发区 486.413 18.924 36 8.633 0.074 高易发区 606.095 23.580 156 37.410 0.257 极高易发区 371.951 14.471 216 51.799 0.581
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[1] Heckerman D. Probabilistic Interpretation for MYCIN s Certainty Factors[C]. Proceedings UAI, 1985.
[2] Shortliffe E H, Buchanan B G. A model of inexact reasoning in medicine[J]. Mathematical Biosciences, 1975, 23: 351 − 379.
[3] 陈绪钰, 李明辉, 王德伟, 等, 2019. 基于GIS和信息量法的四川峨眉山市地质灾害易发性定量评价[J]. 沉积与特提斯地质, 39（4）: 100 − 112
Chen X Y, Li M H, Wang D W, et al., 2019. Quantitative evalution of geohazards susceptibility based on GIS and information value model for Emeishan City, Sichuan. Sedimentary Geology and Tethyan Geology, 39（4）: 100 − 112.
[4] 程维明, 周成虎, 柴慧霞, 等, 2009. 中国陆地地貌基本形态类型定量提取与分析[J]. 地球信息科学学报, （6）: 725-736 doi: 10.3969/j.issn.1560-8999.2009.06.007
Cheng W M, Zhou C H, Chai H X, et al. , 2009. Quantitative extraction and analysis of basic morphological types of land geomorphology in China[J]. Journal of Geo-Information Science, 11（6）: 725-736. doi: 10.3969/j.issn.1560-8999.2009.06.007
[5] 冯新科, 2014. 基于GIS的地震地质灾害危险性评价: 以彝良县为例[D]. 北京: 中国地震局地质研究所.
Feng X K, 2014. Risk assessiment of earthquake-induced geological hazards based GIS -A Case Study of Yiliang County. Beijing: Institute of Geology, China Earthquake Administration.
[6] 郭芳芳, 杨农, 孟晖, 等, 2008. 地形起伏度和坡度分析在区域滑坡灾害评价中的应用[J]. 中国地质, . 35（1）: 131-144.
Guo F F, Yang N, Meng H, et al. , 2008. Application of the relief amplitude and slope analysis to regional landslide hazard assessments[J]. Geology in China, 35（1）: 131-143.
[7] 贺鹏, 童立强, 郭兆成, 等, 2016. GIS支持下基于层次分析法的西藏札达地区滑坡灾害易发性评价研究[J]. 科学技术与工程, 16（25）: 193-200 doi: 10.3969/j.issn.1671-1815.2016.25.033
He P, Tong L Q, Guo Z C, et al. , 2016. Evaluation research on the landslide disaster liability in Zha Da Region of Xizang[J]. Science Technology and Engineering, 16（25）: 193-200. doi: 10.3969/j.issn.1671-1815.2016.25.033
[8] 洪增林, 李永红, 张玲玉, 等, 2020. 一种基于主成分分析法的区域性地质灾害危险性评估方法[J]. 灾害学, 118-124.
Hong Z L, Li Y H, Zhang L Y, et al. , 2020. A method of regional geological hazard assessment based on princeiple component analysis [J]. Jourmal of Catastrophology, 35（1）: 118-124.
[9] 胡凯衡, 崔鹏, 韩用顺, 等, 2012. 基于聚类和最大似然法的汶川灾区泥石流滑坡易发性评价[J]. 中国水土保持科学, 12 − 18.
Hu K H, Cui F, Han Y S, et al., 2012. Susceptibility mapping of landslides and debris flows in 2008 Wenchuan earthquake by using cluster analysis and maximum likelihood classification methods. Science of Soil and Water Conservation, 10（1）: 12 − 18.
[10] 黄润秋, 许向宁, 唐川, 等, 2008. 地质环境评价与地质灾害管理[M]. 北京: 科学出版社.
Huang R Q, Xu X N, Tang C, et al., 2008. Assessment of the Geological Environment and Geo-hazard Management. Beijing: Science Press.
[11] 李鑫, 2009. 台山市崩滑地质灾害风险评价与风险管理研究[D]. 中山大学.
Li X, 2009. Development mechanism of slope geological disasters and risk assessment of slopes in TaiShan. Guangzhou: Zhongshan University.
[12] 刘艳芳, 方佳琳, 陈晓慧, 等, 2014. 基于确定性系数分析方法的秭归县滑坡易发性评价[J]. 自然灾害学报, 23（6）: 209-217 doi: 10.13577/j.jnd.2014.0626
Liu Y F, Fang J L, Chen X H, et al. , 2014. Evaluation of landslide susceptibility in Zigui County based on certainty factor method [J]. Journal of Natural Disasters, 23（6）: 209-217. doi: 10.13577/j.jnd.2014.0626
[13] 饶品增, 曹冉, 蒋卫国, 2017. 基于地理加权回归模型的云南省地质灾害易发性评价[J]. 自然灾害学报, 26（2）: 134-143 doi: 10.13577/j.jnd.2017.0216
Rao P Z, Cao R, Jiang W G, 2017. Susceptibility evaluation of geological disasters in Yunnan Province based on geographically weighted regression model[J]. Journal of Natural Disasters, 26（2）: 134-143. doi: 10.13577/j.jnd.2017.0216
[14] 沈玲玲, 刘连友, 许冲, 等, 2016. 基于多模型的滑坡易发性评价——以甘肃岷县地震滑坡为例[J]. 工程地质学报, 19-28.
Shen L L, Liu L Y, Xu C, et al. , 2016: Multi-models based landslide susceptibility evaluation illustrated with landslides triggered by Minxian earthquake. Journal of Engineering Geology, 24（1）: 19-28.
[15] 覃乙根, 杨根兰, 江兴元, 等, 2020. 基于确定性系数模型与逻辑回归模型耦合的地质灾害易发性评价——以贵州省开阳县为例[J]. 科学技术与工程, 20（1）: 96-103 doi: 10.3969/j.issn.1671-1815.2020.01.015
Qin Y G, Yang G L, Jiang X Y, et al. , 2020. Geohazard susceptibility assessment based on the integrated model of certainty factor model and logistic regression model: a case study in Kaiyang County, Guizhou Province [J]. Science Technology and Engineering, 20（1）: 96-103. doi: 10.3969/j.issn.1671-1815.2020.01.015
[16] 汤国安, 李发源, 刘学军, 2016. 数字高程模型教程（第3版）[M]. 北京: 科学出版社.
Tang G A, Li F Y, Liu X J, 2016. Digital Elevation Model Tutorial[M]. 3rd ed. Beijing: Science Press.
[17] 田春山, 刘希林, 汪佳, 2016. 基于CF和Logistic回归模型的广东省地质灾害易发性评价[J]. 水文地质工程地质, 43（6）: 154-161 doi: 10.16030/j.cnki.issn.1000-3665.2016.06.24
Tian C S, Liu X L, Wang J, 2016. Geohazard susceptibility assessment based on CF model and logistic regression models in Guangdong[J]. Hydrogeology & Engineering Geology, 43（6）: 154-161. doi: 10.16030/j.cnki.issn.1000-3665.2016.06.24
[18] 向喜琼, 黄润秋, 2000. 基于GIS的人工神经网络模型在地质灾害危险性区划中的应用[J]. 中国地质灾害与防治学报, 11（3）: 23 − 27
Xiang X Q , Huang R Q, 2000. Application of GIS-based artificial neural networks on assessment of geohazards risk. The Chinese Journal of Geological Hazard and Control, 11（3）: 23 − 27.
[19] 杨德宏, 范文, 2015. 基于ARCGIS的地质灾害易发性分区评价——以旬阳县为例[J]. 中国地质灾害与防治学报, 26（4）: 82 − 86
Yang D H, Fan W, 2015. Zoning of probable occurrence level of geological disasters based on ArcGIS: a case of Xunyang. The Chinese Journal of Geological Hazard and Control, 26（4）: 82-86.
[20] 赵艳南, 牛瑞卿, 2010. 基于证据权法的滑坡危险性区划探索[J]. 地理与地理信息科学, 26（6）: 19 − 23
Zhao Y N, Niu R Q, 2010. Exploration of Landslide Hazard Zonation Based on the Weights of Evidence Method. Geography and Geo-Information Science, 26（6）: 19 − 23.
[21] 钟亩锋, 2019. 基于确定性系数与Logistic回归模型的地质灾害易发性评价[J]. 世界有色金属, （23）: 186 − 190
Zhong M F, 2019. Geological Hazard Susceptibility Evaluation Based on Certainty Factor and Logistic Regression Model. World Nonferrous Metals, （23）: 186 − 190.
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