Landslide hazard assessment based on improved catastrophe theory
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摘要: 滑坡危险性评价是滑坡风险评估的重要组成部分,对滑坡的预测和防治意义重大。传统滑坡危险性评价在计算指标间重要性时多采用AHP、专家评判法、模糊综合评判等方法, 但存在主观性较强,计算较为复杂等问题。研究基于一种改进的突变理论模型对滑坡进行危险性评价,选取坡度、坡向、高程、平面曲率、剖面曲率、距河流距离、地层岩性、土地利用类型、距断层距离、植被覆盖率、24 h降雨以及人类工程活动等12 个因子作为滑坡危险性评价的影响因子,采用熵权法判定指标间的相对重要性,并建立滑坡危险性评价体系;然后对指标进行标准化、归一化,计算总突变结果;最后使用拟合函数对总突变结果进行转换,得到新的滑坡危险性评价准则,并以雅安市的20 条滑坡对评价准则进行实例验证。结果表明,突变理论得到的评价结果准确率为90%,评价结果更加直观准确。Abstract: Landslide hazard assessment is an important part of landslide risk assessment, which is of great significance to landslide prediction and prevention. Analytic Hierarchy Process(AHP), expert evaluation, fuzzy comprehensive evaluation and other methods were often used in traditional landslide hazard evaluation to calculate the importance of inter-index, which were subjective and complicated. This paper introduced an improved model of mutation theory, which overcame the limitation of traditional methods and achieved higher evaluation accuracy. Firstly, according to field investigation and previous studies, 12 factors including slope, slope direction, elevation, plane curvature, profile curvature, distance from river, stratigraphic lithology, land use type, distance from fault, vegetation coverage rate, 24 h rainfall and human engineering activities were selected as influencing factors of landslide risk assessment, and the relative importance of indicators was determined by entropy weight method, and the landslide risk assessment system was established. Then the index was standardized and normalized, and the total mutation result was calculated. Finally, the fitting function was used to transform the total catastrophe result, and a new criterion of landslide risk assessment was obtained. Taking 20 landslides in Ya’an city as an example, the results showed that the accuracy of the evaluation results obtained by the catastrophe theory was 90%, and the improved evaluation results were more intuitive and accurate
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Keywords:
- landslide /
- risk assessment /
- catastrophe theory /
- entropy weight method
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表 1 一维状态变量的突变模型
Table 1 Mutation model of one-dimensional state variables
突变模型 控制变量维数 势函数 归一公式 折叠突变 1 尖点突变 2 燕尾突变 3 表 2 研究区滑坡的各评价指标
Table 2 Evaluation indexes of landslide in the study area
滑坡点 24 h降雨
/mm地层岩性 距断层距离
/km土地利用
类型坡度
/(°)高程
/m坡向
/ (°)平面曲率 剖面曲率 距河流距离
/km植被
覆盖率1 17 砂岩 3.1176 有林地 11.5042 1403 22.8906 −0.5440 0.5321 0.3168 0.3029 2 2 砾岩 7.0588 灌木林 11.9137 1002 99.0903 −0.5742 0.1138 0.1740 0.1429 3 16 砂岩 11.1765 旱地 9.6462 789 11.3099 0.0217 0.1496 0.1740 −0.1176 4 9 砂岩 3.8235 疏林地 11.2428 968 326.9761 −0.0350 0.0433 0.1020 −0.0078 5 18 砂岩 7.0588 旱地 27.0311 696 210.9638 0.1002 −0.1583 0.4400 0.2735 6 12 砾岩 0.4118 旱地 15.9518 809 122.7352 0.0380 0.2003 0.0900 0.3369 7 18 砂岩 5.0588 水田 24.1319 1827 170.3625 0.0489 −0.0072 0.2800 0.4900 8 6 砂岩 7.6471 高覆盖度草地 16.7599 815 255.5792 −0.0965 0.0915 0.0432 0.4749 9 8 泥岩 2.3529 旱地 20.9576 1574 81.8699 −0.0307 −0.1013 0.2720 0.3189 10 17 泥岩 6.4706 中覆盖度草地 20.8143 1103 243.9967 −0.1543 −0.0356 0.1260 0.2003 11 3 砂岩 4.7059 城镇用地 12.9588 649 58.3245 0.1499 −0.0642 0.0300 −0.0732 12 17 砂岩 2.6471 有林地 20.7455 1205 124.3151 0.0250 −0.1204 0.3000 0.3348 13 15 砂岩 9.0000 中覆盖度草地 23.0888 2094 85.5154 −0.0577 −0.2289 8.6000 0.1837 14 18 砂岩 4.1176 旱地 7.8539 611 25.0169 −0.0866 0.2141 0.0800 0.1813 15 13 冲洪积砾石及砂土 2.2353 旱地 18.4350 1086 180.0000 −0.1286 −0.1171 0.1640 0.0000 16 5 砂岩 8.5294 旱地 18.5686 726 60.2551 0.0832 −0.0702 0.1000 0.2671 17 22 冲洪积砾石及砂土 2.3529 旱地 7.1172 1096 334.2900 −0.1590 0.4979 0.1680 0.3975 18 32 砂岩 8.4706 旱地 29.2601 1769 59.6209 0.1344 −0.0270 6.5000 0.4317 19 34 冲洪积砾石及砂土 8.8235 旱地 14.7525 576 85.4622 0.1103 0.0115 0.0440 0.2170 20 34 砂岩 7.0588 中覆盖度草地 14.7242 889 267.2737 0.3683 −0.3448 0.3120 0.3745 表 3 滑坡危险性评价体系
Table 3 Landslide risk assessment system
目标层 突变模型 准则层 突变模型 中间层 突变模型 指标层 滑坡危险性A 燕尾突变(非互补) 地形地貌B1 尖点突变(非互补) 地貌C1 燕尾突变(非互补) 剖面曲率D1 平面曲率D2 坡向D3 滑坡形态C2 尖点突变(互补) 高程D4 坡度D5 地质条件B2 燕尾突变(非互补) 岩性条件C3 折叠突变 地层岩性D6 构造条件C4 尖点突变(非互补) 距断层距离D7 距河流距离D8 植被条件C5 折叠突变 植被覆盖率D9 诱发因素B3 折叠突变 致灾因子C6 燕尾突变(非互补) 24 h降雨D10 土地利用类型D11 人类工程活动D12 表 4 底层指标
与总突变结果 对应关系Table 4 Corresponding relationship between underlying indicators
and total mutation resultsx 0.00 0.05 0.10 0.15 0.20 0.25 0.30 y 0.0000 0.5866 0.7407 0.7900 0.8215 0.8451 0.8640 x 0.35 0.40 0.45 0.50 0.55 0.60 0.65 y 0.8799 0.8937 0.9058 0.9167 0.9266 0.9356 0.9440 x 0.70 0.75 0.80 0.85 0.90 0.95 1.00 y 0.9517 0.9590 0.9658 0.9723 0.9784 0.9842 0.9897 表 5 标准化结果
Table 5 Standardization results
序号 剖面曲率 平面曲率 坡向 高程 坡度 地层岩性 距断层距离 距河流距离 植被覆盖率 降雨 土地利用类型 人类工程活动 1 0.9448 0.0845 0.0749 0.4956 0.1978 0.90 0.7696 0.9693 0.3532 0.4299 0.70 0.89 2 0.5112 0.0554 0.5236 0.2709 0.2137 0.60 0.4391 0.9844 0.5926 0.0050 0.60 0.34 3 0.5483 0.6302 0.0067 0.1516 0.1257 0.90 0.0937 0.9844 0.9824 0.4142 0.50 0.35 4 0.4381 0.5755 0.2170 0.2519 0.1876 0.90 0.7104 0.9920 0.8180 0.1928 0.80 0.30 5 0.2291 0.7059 0.8351 0.0995 0.8000 0.90 0.4391 0.9562 0.3973 0.4484 0.50 0.40 6 0.6009 0.6459 0.6628 0.1628 0.3703 0.60 0.9965 0.9933 0.3023 0.3021 0.50 0.33 7 0.3858 0.6563 0.9432 0.7331 0.6876 0.90 0.6068 0.9732 0.0733 0.4619 0.40 0.80 8 0.4880 0.5161 0.5974 0.1662 0.4016 0.90 0.3897 0.9983 0.0958 0.1115 1.10 0.32 9 0.2882 0.5796 0.4222 0.5914 0.5645 1.40 0.8337 0.9740 0.3293 0.1648 0.50 0.32 10 0.3563 0.4604 0.6591 0.3275 0.5589 1.40 0.4884 0.9895 0.5067 0.4299 1.20 0.32 11 0.3266 0.7538 0.2835 0.0732 0.2542 0.90 0.6364 0.9997 0.9159 0.0415 0.10 0.20 12 0.2684 0.6333 0.6721 0.3846 0.5562 0.90 0.8091 0.9711 0.3054 0.4299 0.70 0.30 13 0.1559 0.5536 0.4436 0.8827 0.6471 0.90 0.2763 0.0912 0.5315 0.3869 1.20 0.48 14 0.6152 0.5257 0.0874 0.0519 0.0562 0.90 0.6857 0.9944 0.5351 0.4485 0.50 0.31 15 0.2718 0.4852 1.0000 0.3180 0.4666 1.50 0.8436 0.9855 0.8064 0.3077 0.50 0.32 16 0.3204 0.6895 0.2949 0.1163 0.4718 0.90 0.3157 0.9923 0.4068 0.0893 0.50 0.30 17 0.9093 0.4558 0.1781 0.3236 0.0276 1.50 0.8337 0.9851 0.2117 0.5660 0.50 0.30 18 0.3652 0.7389 0.2911 0.7006 0.8865 0.90 0.3207 0.3138 0.1605 0.8430 0.50 0.46 19 0.4051 0.7156 0.4433 0.0323 0.3238 1.50 0.2911 0.9982 0.4817 0.9048 0.50 0.35 20 0.0357 0.9645 0.5351 0.2076 0.3227 0.90 0.4391 0.9698 0.2461 0.9050 1.20 0.40 表 6 滑坡危险性评价准则
Table 6 Criteria for landslide hazard assessment
危险性级别 高危险 中危险 低危险 改进前 (0.9100, 1] (0.8500, 0.9100] (0, 0.8500] 改进后 (0.4798, 1] (0.2916, 0.4798] (0, 0.2916] 表 7 滑坡危险性评价结果
Table 7 Landslide risk assessment results
序号 改进前 危险性 改进后 危险性 现场调查结果 1 0.8057 低危险 0.2019 低危险 低危险 2 0.6435 低危险 0.0526 低危险 低危险 3 0.6586 低危险 0.0596 低危险 低危险 4 0.8561 中危险 0.3068 中危险 中危险 5 0.8880 中危险 0.3998 中危险 中危险 6 0.8654 中危险 0.3313 中危险 高危险 7 0.8493 低危险 0.2900 低危险 低危险 8 0.8330 低危险 0.2531 低危险 低危险 9 0.8605 中危险 0.3181 中危险 中危险 10 0.9140 高危险 0.4961 高危险 高危险 11 0.7670 低危险 0.1465 低危险 低危险 12 0.9216 高危险 0.5281 高危险 高危险 13 0.9019 中危险 0.4486 中危险 中危险 14 0.7434 低危险 0.1204 低危险 低危险 15 0.9046 中危险 0.4589 中危险 中危险 16 0.8177 低危险 0.2230 低危险 低危险 17 0.8124 低危险 0.2134 低危险 中危险 18 0.8919 中危险 0.4130 中危险 中危险 19 0.8113 低危险 0.2114 低危险 低危险 20 0.8310 低危险 0.2492 低危险 低危险 -
[1] 汤明高,吴川,吴辉隆,等. 水库滑坡地下水动态响应规律及浸润线计算模型—以石榴树包滑坡为例[J]. 水文地质工程地质,2022,49(2):115 − 125. [TANG Minggao,WU Chuan,WU Huilong,et al. Dynamic response law of groundwater in reservoir landslide and calculation model of infiltration line: A case study of Shidshubao landslide[J]. Hydrogeology & Engineering Geology,2022,49(2):115 − 125. (in Chinese with English abstract) [2] KALANTAR B,PRADHAN B,NAGHIBI S A,et al. Assessment of the effects of training data selection on the landslide susceptibility mapping:a comparison between support vector machine (SVM),logistic regression (LR) and artificial neural networks (ANN)[J]. Geomatics,Natural Hazards and Risk,2018,9(1):49 − 69. DOI: 10.1080/19475705.2017.1407368
[3] 张钟远,邓明国,徐世光,等. 镇康县滑坡易发性评价模型对比研究[J]. 岩石力学与工程学报,2022,41(1):157 − 171. [ZHANG Zhongyuan,DENG Mingguo,XU Shiguang,et al. Comparative study on evaluation models of landslide susceptibility in Zhenkang County[J]. Chinese Journal of Rock Mechanics and Engineering,2022,41(1):157 − 171. (in Chinese with English abstract) [4] SHANO L,RAGHUVANSHI T K,METEN M. Landslide susceptibility mapping using frequency ratio model:The case of Gamo highland,South Ethiopia[J]. Arabian Journal of Geosciences,2021,14(7):1 − 18.
[5] MAŁKA A N. Landslide susceptibility mapping of Gdynia using geographic information system-based statistical models[J]. Natural Hazards,2021,107(1):639 − 674. DOI: 10.1007/s11069-021-04599-8
[6] 祁于娜,王磊. 层次分析-熵值定权法应用于山区城镇地质灾害易发性评价[J]. 测绘通报,2021(6):112 − 116. [QI Yuna,WANG Lei. Application of AHP-entropy weight method in hazards susceptibility assessment in mountain town[J]. Bulletin of Surveying and Mapping,2021(6):112 − 116. (in Chinese with English abstract) DOI: 10.13474/j.cnki.11-2246.2021.0187 [7] 吴博,赵法锁,段钊,等. 基于熵权的属性识别模型在陕西土质滑坡危险度评价中的应用[J]. 灾害学,2018,33(1):140 − 145. [WU Bo,ZHAO Fasuo,DUAN Zhao,et al. Application of attribute recognition model based on coefficient of entropy to hazard degree evaluation of soil landslide in Shaanxi[J]. Journal of Catastrophology,2018,33(1):140 − 145. (in Chinese with English abstract) DOI: 10.3969/j.issn.1000-811X.2018.01.025 [8] ZHU J Q ,LI T Z . Catastrophe theory-based risk evaluation model for water and mud inrush and its application in Karst tunnels[J]. Journal of Central South University,2020,27(5):1587 − 1598. DOI: 10.1007/s11771-020-4392-0
[9] 宋盛渊, 王清, 潘玉珍, 等. 基于突变理论的滑坡危险性评价[J]. 岩土力学, 2014, 35(增刊2): 422 − 428 SONG Shengyuan, WANG Qing, PAN Yuzhen, et al. Evaluation of landslide susceptibility degree based on catastrophe theory[J]. Rock and Soil Mechanics, 2014, 35(Sup 2): 422 − 428. (in Chinese with English abstract)
[10] 王雪冬,叶果,李世宇,等. 基于熵值法和突变级数法的泥石流易损度评价[J]. 地质与资源,2019,28(5):493 − 496. [WANG Xuedong,YE Guo,LI Shiyu,et al. Vulnerability assessment of debris flow based on entropy value and catastrophe progression methods[J]. Geology and Resources,2019,28(5):493 − 496. (in Chinese with English abstract) DOI: 10.3969/j.issn.1671-1947.2019.05.013 [11] 刘晓宇,任光明,刘彬,等. 基于突变理论的滑坡危险性评价[J]. 西华大学学报(自然科学版),2020,39(2):95 − 99. [LIU Xiaoyu,REN Guangming,LIU Bin,et al. Analysis of landslide hazard based on mutation series method[J]. Journal of Xihua University (Natural Science Edition),2020,39(2):95 − 99. (in Chinese with English abstract) [12] MOGAJI K A,LIM H S. Development of a GIS-based catastrophe theory model (modified DRASTIC model) for groundwater vulnerability assessment[J]. Earth Science Informatics,2017,10(3):339 − 356. DOI: 10.1007/s12145-017-0300-z
[13] GHORBANI M A,KHATIBI R,SIVAKUMAR B,et al. Study of discontinuities in hydrological data using catastrophe theory[J]. Hydrological Sciences Journal,2010,55(7):1137 − 1151. DOI: 10.1080/02626667.2010.513477
[14] QIU X X,CAO Q G,WANG Y N,et al. Risk assessment method of coal spontaneous combustion based on catastrophe theory[J]. IOP Conference Series:Earth and Environmental Science,2020,603(1):012017. DOI: 10.1088/1755-1315/603/1/012017
[15] 王艺洁,张东映,张小清,等. 基于改进突变评价法的安徽省旱灾风险评价[J]. 水电能源科学,2020,38(11):1 − 4. [WANG Yijie,ZHANG Dongying,ZHANG Xiaoqing,et al. Drought risk assessment of Anhui Province based on improved catastrophe progression approach[J]. Water Resources and Power,2020,38(11):1 − 4. (in Chinese with English abstract) [16] 夏杰塬. 改进的突变评价法在河南省农业干旱中的应用[D]. 郑州: 华北水利水电大学, 2017 XIA Jieyuan. Application of improved catastrophe evaluation method in agricultural drought in Henan Province[D]. Zhengzhou: North China University of Water Resources and Electric Power, 2017. (in Chinese with English abstract)
[17] 赵晓燕,谈树成,李永平. 基于斜坡单元与组合赋权法的东川区地质灾害危险性评价[J]. 云南大学学报(自然科学版),2021,43(2):299 − 305. [ZHAO Xiaoyan,TAN Shucheng,LI Yongping. Risk assessment of geological hazards in Dongchuan District based on the methods of slope unit and combination weighting[J]. Journal of Yunnan University (Natural Sciences Edition),2021,43(2):299 − 305. (in Chinese with English abstract) [18] 梁桂兰,徐卫亚,何育智,等. 突变级数法在边坡稳定综合评判中的应用[J]. 岩土力学,2008,29(7):1895 − 1899. [LIANG Guilan,XU Weiya,HE Yuzhi,et al. Application of catastrophe progression method to comprehensive evaluation of slope stability[J]. Rock and Soil Mechanics,2008,29(7):1895 − 1899. (in Chinese with English abstract) DOI: 10.3969/j.issn.1000-7598.2008.07.031 [19] 唐然,邓韧,董建辉,等. 雅安市汉源县永定桥水库飞水沟滑坡成因机制分析[J]. 地质论评,2015,61(增刊 1):110 − 111. [Tang Ran,Deng Ren,Dong Jianhui,et al. Genetic mechanism analysis of Feishuigou landslide in Yongdingqiao Reservoir, Hanyuan County, Ya’an City[J]. Geological Review,2015,61(Sup 1):110 − 111. (in Chinese with English abstract) [20] 李鹏岳,巴仁基,倪化勇,等. 库水位升降速率对雅安双家坪堆积体滑坡稳定性影响模拟分析[J]. 地质力学学报,2017,23(2):288 − 295. [LI Pengyue,BA Renji,NI Huayong,et al. Simulation analysis of the influence of water level rise and fall rate on the stability of Shuangjiaping accumulation in Ya’an[J]. Chinese Journal of Geomechanics,2017,23(2):288 − 295. (in Chinese with English abstract) [21] 徐晓雪,季灵运,张文婷,等. 基于相干性的InSAR时间序列方法追溯四川雅安地区汉源滑坡灾前形变[J]. 地球科学与环境学报,2022,44(4):632 − 640. [XU Xiaoxue,JI Lingyun,ZHANG Wenting,et al. Trace deformation of Hanyuan Landslide in Ya 'an Area, Sichuan Province based on InSAR time series method of coherence[J]. Journal of Earth Sciences and Environment,2022,44(4):632 − 640. (in Chinese with English abstract) [22] 侯圣山,李昂,韩冰,等. 四川雅安地质灾害预警预报及分析[J]. 中国地质灾害与防治学报,2014,25(4):134 − 138. [HOU Shengshan,LI Ang,HAN Bing,et al. Prediction and analysis of geological hazards in Ya’an, Sichuan Province[J]. The Chinese Journal of Geological Hazards and Control,2014,25(4):134 − 138. (in Chinese with English abstract) [23] 方然可,刘艳辉,苏永超,等. 基于逻辑回归的四川青川县区域滑坡灾害预警模型[J]. 水文地质工程地质,2021,48(1):181 − 187. [FANG Ranke,LIU Yanhui,SU Yongchao,et al. Prediction model of regional landslide disaster in Qingchuan County, Sichuan Province based on logistic regression[J]. Hydrogeology & Engineering Geology,2021,48(1):181 − 187. (in Chinese with English abstract) [24] 刘福臻,王灵,肖东升. 机器学习模型在滑坡易发性评价中的应用[J]. 中国地质灾害与防治学报,2021,32(6):98 − 106. [LIU Fuzhen,WANG Ling,XIAO Dongsheng. Application of machine learning model in landslide susceptibility evaluation[J]. The Chinese Journal of Geological Hazard and Control,2021,32(6):98 − 106. (in Chinese with English abstract) [25] 杨华阳,许向宁,杨鸿发. 基于证据权法的九寨沟地震滑坡危险性评价[J]. 中国地质灾害与防治学报,2020,31(3):20 − 29. [YANG Huayang,XU Xiangning,YANG Hongfa. The Jiuzhaigou co-seismic landslide hazard assessment based on weight of evidence method[J]. The Chinese Journal of Geological Hazard and Control,2020,31(3):20 − 29. (in Chinese with English abstract) DOI: 10.16031/j.cnki.issn.1003-8035.2020.03.03 [26] 周天伦,曾超,范晨,等. 基于快速聚类-信息量模型的汶川及周边两县滑坡易发性评价[J]. 中国地质灾害与防治学报,2021,32(5):137 − 150. [ZHOU Tianlun,ZENG Chao,FAN Chen,et al. Landslide susceptibility assessment based on K-means cluster information model in Wenchuan and two neighboring counties,China[J]. The Chinese Journal of Geological Hazard and Control,2021,32(5):137 − 150. (in Chinese with English abstract) DOI: 10.16031/j.cnki.issn.1003-8035.2021.05-17 -
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