Susceptibility assessment of debris flows based on information model in Dongchuan, Yunnan Province
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摘要:
以东川泥石流为研究对象，选取高程、坡度、坡向、起伏度、曲率、工程岩组、距断层距离、距水系距离、土地利用类型9个影响因子，以研究区144条泥石流为样本数据，建立了东川泥石流易发性评价体系。基于GIS平台，采用信息量模型计算各个评价指标状态分级的信息量值，以小流域为评价单元使用自然间断法将研究区泥石流易发程度分为极高、高、中和低4个易发区等级。结果表明：研究区极高易发区和高易发区发生泥石流灾害数量占比94.44%，AUC值为0.876，表明选取评价指标合理，信息量模型适用于东川泥石流易发性评价研究。
Abstract:In this paper, taking debris flow in Dongchuan as the research object, nine influence factors are chosen as the selected indices, including the elevation, slope, aspect, relief, curvature, engineering rock group, distance to faults, distance to faults rivers, and land use types, sample data from 144 debris flows in the study area are used to establish the Dongchuan debris flow susceptibility assessment system. Based on the information model and GIS platform, the information vaule of each factor classification is calculated, and the natural discontinuity method is used to divide the debris flow susceptibility into 4 levels: extremely high-prone areas, high-prone areas, medium-prone areas, and low-prone areas in the study area. The results show that the number of debris flow disasters in the extremely high and high-risk areas in the study area accounted for 94.44%, and the AUC value was 0.876, indicating that the selection of evaluation indicators was reasonable, and the information model was suitable for the evaluation of debris flow susceptibility in Dongchuan.
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Key words:
- debris flow /
- susceptibility assessment /
- information model /
- Dongchuan
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表 1 数据来源及类型
Table 1. Data source and types
基础数据 评价因子 数据来源及制作 数据格式 DEM 高程 ASF
（阿拉斯加
卫星设备）12.5 m×12.5 m
栅格数据坡度 坡向 起伏度 曲率 水系 距水系距离 DEM提取
Open Street Map矢量数据 地质数据 工程岩组分类 全国地质资料馆 矢量数据 距断层距离 土地类型 土地利用类型 ESA WorldCover 10 m栅格数据 灾害点 泥石流数量 地质灾害详查、排查等 矢量数据 
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表 2 各因素状态信息量表
Table 2. Weighted information values of each factor
指标因子 分级 泥石流点比例 信息量值 指标因子 分级 泥石流点比例 信息量值 高程/m 660～1500 0.2033 1.238744 曲率 −38～−1 0.2139 −0.131543 1500～2000 0.2598 −0.577567 −1～0 0.3114 0.430833 2000～2500 0.2209 −1.380367 0～2 0.3838 −0.205470 2500～3000 0.1594 −0.831065 >2 0.0909 −1.473365 >3000 0.1566 −1.729497 工程岩组 软岩组 0.0277 1.255712 坡度/（°） 0～10 0.1137 1.315538 较软岩组 0.5647 0.067643 10～20 0.2133 0.545702 较坚硬岩组 0.0693 −0.220774 20～30 0.2821 −0.871237 坚硬岩组 0.3383 −0.330859 30～40 0.2553 −1.206583 距水系距离/m 0 0.0037 1.322303 >40 0.1356 −2.278888 200 0.5022 0.624230 坡向 平坦（−1） 0.0011 0.000000 400 0.2786 −1.899977 北（0～22.5） 0.0638 0.084068 >400 0.2155 −3.435165 北东（22.5～67.5） 0.1268 0.353573 距断层距离/m <1000 0.6126 0.183653 东（67.5～112.5） 0.136 0.392672 1000～2000 0.2277 −0.445741 南东（112.5～157.5） 0.1266 −0.337806 2000～3000 0.0933 0.041246 南（157.5～202.5） 0.1072 −0.791193 >3000 0.0664 −1.158486 南西（202.5~247.5） 0.1119 −0.214621 土地利用类型 林地 0.2839 0.184122 西（247.5～292.5） 0.1379 0.269687 灌木 0.0024 −1.048475 北西（292.5～337.5） 0.1269 −0.266767 草地 0.4877 0.955946 北（337.5～360） 0.0618 −0.394293 耕地 0.1166 −0.437008 起伏度/（°） 0～20 0.2639 1.006637 建筑用地 0.0230 −1.937148 20～40 0.4084 −0.577548 裸地/稀疏植被区 0.0822 −0.994439 40～60 0.2465 −1.960151 开阔水域 0.0042 −0.514259 60～441 0.0812 −1.766161
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[1] 岳溪柳,黄玫,徐庆勇,等. 贵州省喀斯特地区泥石流灾害易发性评价[J]. 地球信息科学学报,2015,17(11):1395 − 1403. [YUE Xiliu,HUANG Mei,XU Qingyong,et al. The susceptibility assessment of debris flow in karst region of Guizhou Province[J]. Journal of Geo-Information Science,2015,17(11):1395 − 1403. (in Chinese with English abstract)
[2] 陈剑,黎艳,许冲. 金沙江干热河谷区泥石流易发性评价模型及应用[J]. 山地学报,2016,34(4):460 − 467. [CHEN Jian,LI Yan,XU Chong. Susceptibility assessment model of debris flows in the dry-hot valley of the Jinsha River and its application[J]. Mountain Research,2016,34(4):460 − 467. (in Chinese with English abstract)
[3] 王劲峰. 中国自然灾害区划: 灾害区划、影响评价、减灾对策[M]. 北京: 中国科学技术出版社, 1995
WANG Jinfeng. Regionalization of hazards in China: Regionalization, assessment and strategies [M]. Beijing: China Science and Technology Press, 1995. （in Chinese）
[4] 殷坤龙,朱良峰. 滑坡灾害空间区划及GIS应用研究[J]. 地学前缘,2001,8(2):279 − 284. [YIN Kunlong,ZHU Liangfeng. Landslide hazard zonation and application of GIS[J]. Earth Science Frontiers,2001,8(2):279 − 284. (in Chinese with English abstract) doi: 10.3321/j.issn:1005-2321.2001.02.010
[5] DYMOND J R,JESSEN M R,LOVELL L R. Computer simulation of shallow landsliding in New Zealand hill country[J]. International Journal of Applied Earth Observation and Geoinformation,1999,1(2):122 − 131. doi: 10.1016/S0303-2434(99)85005-3
[6] JIBSON R W,HARP E L,MICHAEL J A. A method for producing digital probabilistic seismic landslide hazard maps[J]. Engineering Geology,2000,58(3/4):271 − 289. doi: 10.1016/S0013-7952(00)00039-9
[7] TIEN BUI D,PRADHAN B,LOFMAN O,et al. Landslide susceptibility assessment in the Hoa Binh Province of Vietnam:A comparison of the Levenberg-Marquardt and Bayesian regularized neural networks[J]. Geomorphology,2012,171/172:12 − 29. doi: 10.1016/j.geomorph.2012.04.023
[8] PRADHAN B,LEE S. Delineation of landslide hazard areas on Penang Island,Malaysia,by using frequency ratio,logistic regression,and artificial neural network models[J]. Environmental Earth Sciences,2010,60(5):1037 − 1054. doi: 10.1007/s12665-009-0245-8
[9] 胡凯衡,崔鹏,韩用顺,等. 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,2012,10(1):12 − 18. [HU Kaiheng,CUI Peng,HAN Yongshun,et al. Susceptibility mapping of landslides and debris flows in 2008 Wenchuan earthquake by using cluster analysis and maximum likelihood classification methods[J]. Science of Soil and Water Conservation,2012,10(1):12 − 18. (in Chinese with English abstract) doi: 10.3969/j.issn.1672-3007.2012.01.003
[10] 焦方谦,赵新生,陈川. 证据权模型在泥石流灾害易发性评价中的应用[J]. 干旱区地理,2013,36(6):1111 − 1124. [JIAO Fangqian,ZHAO Xinsheng,CHEN Chuan. Debris flow hazard susceptibility evaluation application with weighted evidences model[J]. Arid Land Geography,2013,36(6):1111 − 1124. (in Chinese with English abstract)
[11] 刘磊,殷坤龙,王佳佳,等. 降雨影响下的区域滑坡危险性动态评价研究—以三峡库区万州主城区为例[J]. 岩石力学与工程学报,2016,35(3):558 − 569. [LIU Lei,YIN Kunlong,WANG Jiajia,et al. Dynamic evaluation of regional landslide hazard due to rainfall:A case study in Wanzhou central district,Three Gorges Reservoir[J]. Chinese Journal of Rock Mechanics and Engineering,2016,35(3):558 − 569. (in Chinese with English abstract)
[12] 刘磊. 三峡水库万州区库岸滑坡灾害风险评价研究[D]. 武汉: 中国地质大学, 2016
LIU Lei. Assessment of landslide risk along Wanzhou area in Three Gorges Reservoir[D]. Wuhan: China University of Geosciences, 2016. （in Chinese with English abstract）
[13] CHEN W,POURGHASEMI H R,KORNEJADY A,et al. Landslide spatial modeling:Introducing new ensembles of ANN,MaxEnt,and SVM machine learning techniques[J]. Geoderma,2017,305:314 − 327. doi: 10.1016/j.geoderma.2017.06.020
[14] ZHOU C,YIN K L,CAO Y,et al. Application of time series analysis and PSO-SVM model in predicting the Bazimen landslide in the Three Gorges Reservoir,China[J]. Engineering Geology,2016,204:108 − 120. doi: 10.1016/j.enggeo.2016.02.009
[15] TIEN BUI D,TUAN T A,KLEMPE H,et al. Spatial prediction models for shallow landslide hazards:A comparative assessment of the efficacy of support vector machines,artificial neural networks,kernel logistic regression,and logistic model tree[J]. Landslides,2016,13(2):361 − 378. doi: 10.1007/s10346-015-0557-6
[16] 吴孝情,赖成光,陈晓宏,等. 基于随机森林权重的滑坡危险性评价:以东江流域为例[J]. 自然灾害学报,2017,26(5):119 − 129. [WU Xiaoqing,LAI Chengguang,CHEN Xiaohong,et al. A landslide hazard assessment based on random forest weight:A case study in the Dongjiang River basin[J]. Journal of Natural Disasters,2017,26(5):119 − 129. (in Chinese with English abstract)
[17] PARK I,LEE S. Spatial prediction of landslide susceptibility using a decision tree approach:A case study of the Pyeongchang area,Korea[J]. International Journal of Remote Sensing,2014,35(16):6089 − 6112. doi: 10.1080/01431161.2014.943326
[18] 庄育龙,田原,程楚云. 基于深度神经网络的滑坡危险性评价:以深圳市为例[J]. 地理与地理信息科学,2019,35(2):104 − 110. [ZHUANG Yulong,TIAN Yuan,CHENG Chuyun. Landslide susceptibility assessment based on deep neural network:A case study of Shenzhen[J]. Geography and Geo-Information Science,2019,35(2):104 − 110. (in Chinese with English abstract) doi: 10.3969/j.issn.1672-0504.2019.02.016
[19] 王佳佳,殷坤龙,肖莉丽. 基于GIS和信息量的滑坡灾害易发性评价—以三峡库区万州区为例[J]. 岩石力学与工程学报,2014,33(4):797 − 808. [WANG Jiajia,YIN Kunlong,XIAO Lili. Landslide susceptibility assessment based on GIS and weighted information value:A case study of Wanzhou District,Three Gorges Reservoir[J]. Chinese Journal of Rock Mechanics and Engineering,2014,33(4):797 − 808. (in Chinese with English abstract)
[20] 胡瑞林,范林峰,王珊珊,等. 滑坡风险评价的理论与方法研究[J]. 工程地质学报,2013,21(1):76 − 84. [HU Ruilin,FAN Linfeng,WANG Shanshan,et al. Theory and method for landslide risk assessment-current status and future development[J]. Journal of Engineering Geology,2013,21(1):76 − 84. (in Chinese with English abstract) doi: 10.3969/j.issn.1004-9665.2013.01.009
[21] 程英建,石豫川,石胜伟,等. 数量化理论在泥石流易发性预测中的应用[J]. 水文地质工程地质,2015,42(1):140 − 145. [CHENG Yingjian,SHI Yuchuan,SHI Shengwei,et al. Prediction of debris flow occurrence based on the quantification theory[J]. Hydrogeology & Engineering Geology,2015,42(1):140 − 145. (in Chinese with English abstract)
[22] 李晨阳, 王新春, 何春珍, 等. 全国1∶200 000 数字地质图（公开版）空间数据库（V1）[DB]. 中国地质调查局发展研究中心, 中国地质调查局, 全国地质资料馆, 1957
LI Chenyang, WANG Xinchun, HE Chunzhen, etal. China national digital geological map （public version at 1∶200000 scale） spatial database （V1）[DB]. Development and Research Center, China Geological Survey, China Geological Survey, National Geological Archives of China, 1957. （in Chinese）
[23] 高克昌,崔鹏,赵纯勇,等. 基于地理信息系统和信息量模型的滑坡危险性评价:以重庆万州为例[J]. 岩石力学与工程学报,2006,25(5):991 − 996. [GAO Kechang,CUI Peng,ZHAO Chunyong,et al. Landslide hazard evaluation of Wanzhou based on GIS information value method in the Three Gorges Reservoir[J]. Chinese Journal of Rock Mechanics and Engineering,2006,25(5):991 − 996. (in Chinese with English abstract) doi: 10.3321/j.issn:1000-6915.2006.05.020
[24] 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. [WANG Ningtao,PENG Ke,LI Qinghua,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)
[25] 牛全福,程维明,兰恒星,等. 基于信息量模型的玉树地震次生地质灾害危险性评价[J]. 山地学报,2011,29(2):243 − 249. [NIU Quanfu,CHENG Weiming,LAN Hengxing,et al. Susceptibility assessment of secondary geological disaster based on information value methodology for Yushu earthquake region[J]. Journal of Mountain Science,2011,29(2):243 − 249. (in Chinese with English abstract) doi: 10.3969/j.issn.1008-2786.2011.02.014
[26] HOYER A,KUSS O. Meta-analysis of full ROC curves with flexible parametric distributions of diagnostic test values[J]. Research Synthesis Methods,2020,11(2):301 − 313. doi: 10.1002/jrsm.1395
[27] WALKER S P. The ROC curve redefined - optimizing sensitivity (and specificity) to the lived reality of cancer[J]. The New England Journal of Medicine,2019,380(17):1594 − 1595. doi: 10.1056/NEJMp1814951
[28] OMAR L,IVRISSIMTZIS I. Using theoretical ROC curves for analysing machine learning binary classifiers[J]. Pattern Recognition Letters,2019,128:447 − 451. doi: 10.1016/j.patrec.2019.10.004
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