基于GIS与信息量模型的溪洛渡库区滑坡危险性评价
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摘要
通过高分辨率影像对溪洛渡库区段金沙江两岸5 km范围内滑坡灾害遥感解译,结合野外实地调查验证,已查明共有滑坡161处。从滑坡形成机理入手,选取高程、相对高差、坡度、坡向、岩性、地震、降雨等7个评价指标,首先分析滑坡与各因素的空间分布关系,然后采用层次分析法和信息量模型对研究区进行滑坡危险性评价。研究结果表明:(1)研究区滑坡易发生在高程600～1 300 m、高差100～700 m、坡度15°～40°的区域,降雨量越大,滑坡发生的概率越大,且坡向对滑坡发生也有影响,其中东、东南、西、西北方向的滑坡相对较多;(2)极高和高危险区占研究区总面积的27.86%,却包含研究区76.4%的滑坡数量,说明这些区域滑坡分布十分密集,危险程度较高,与实际情况相吻合;(3)极高危险区和高危险区主要集中在溪洛渡坝址沿江两岸附近区域、距离坝址约25～35 km的金沙江右岸区域和库尾金沙江左岸20 km范围区域,库区中段大部分区域都是中、低和极低危险区;(4)滑坡灾害点空间分布与危险评价等级之间相关性达0.970 3,利用ROC曲线对评价结果进行验证,评价结果理想,精度高达81.88%,可为库区防灾减灾工作提供理论支撑。
According to the interpretation of high-resolution remote sensing images and field investigation on both sides of Jinsha River in the Xiluodu reservoir area, 161 landslides were identified. This paper started with the mechanism of landslide formation, and seven evaluation indies including elevation, height difference, slope, aspect, lithology, earthquake and rainfall were chosen to evaluate the hazard of landslides by AHP(Analytic Hierarchy Process) and information value method. The spatial distribution relationships between landslides and various factors were analyzed at first. The results showed that:(1) The landslide easily occurs in the regions with the elevation in the range of 600-1300 m, the height difference between 100-700 m, the slope between 15°-40°, and with large rainfall. Moreover, the different slope aspects also had influence on the landslide, and there were relatively more landslides in the east, southeast and northwest direction.(2) The extremely high areas and the high risk areas accounted for 27.86% of the total study area, where 76.4% of the landslides locate, indicating that the landslides were very dense in these areas and the risk was relatively high, which was consistent with the actual situation.(3) The extremely high risk areas and the high risk areas mainly concentrate in the Xiluodu dam site along the river, which was the right bank area of the Jinsha River about 25 to 35 km away from the site of the dam, and the left bank area of the Jinsha River with 20 km away from the reservoir tail. And most of the areas in the middle section of the reservoir were moderate risk areas.(4) The correlation coefficient between the spatial distribution of landslide hazards and the risk assessment grade was 0.970 3. Verified by ROC curves, the evaluation results were ideal with the accuracy of 81.88%, which can provide theoretical supports for the disaster prevention and mitigation work in the reservoir area.
According to the interpretation of high-resolution remote sensing images and field investigation on both sides of Jinsha River in the Xiluodu reservoir area, 161 landslides were identified. This paper started with the mechanism of landslide formation, and seven evaluation indies including elevation, height difference, slope, aspect, lithology, earthquake and rainfall were chosen to evaluate the hazard of landslides by AHP(Analytic Hierarchy Process) and information value method. The spatial distribution relationships between landslides and various factors were analyzed at first. The results showed that:(1) The landslide easily occurs in the regions with the elevation in the range of 600-1300 m, the height difference between 100-700 m, the slope between 15°-40°, and with large rainfall. Moreover, the different slope aspects also had influence on the landslide, and there were relatively more landslides in the east, southeast and northwest direction.(2) The extremely high areas and the high risk areas accounted for 27.86% of the total study area, where 76.4% of the landslides locate, indicating that the landslides were very dense in these areas and the risk was relatively high, which was consistent with the actual situation.(3) The extremely high risk areas and the high risk areas mainly concentrate in the Xiluodu dam site along the river, which was the right bank area of the Jinsha River about 25 to 35 km away from the site of the dam, and the left bank area of the Jinsha River with 20 km away from the reservoir tail. And most of the areas in the middle section of the reservoir were moderate risk areas.(4) The correlation coefficient between the spatial distribution of landslide hazards and the risk assessment grade was 0.970 3. Verified by ROC curves, the evaluation results were ideal with the accuracy of 81.88%, which can provide theoretical supports for the disaster prevention and mitigation work in the reservoir area.
引文
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[25] 张夏冉,殷坤龙,李烨,等.库水位下降和降雨对重庆万州下坪滑坡稳定性的影响[J].中国地质灾害与防治学报,2017,28(2):22-29.ZHANG X R,YIN K L,LI Y,et al. Influence of drawdown of reservoir water level and rainfall on Xiaping landslide stability[J]. Chinese Journal of Geological Hazard & Control,2017,28(2):22-29.
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[2] 谈树成,赵晓燕,李永平,等.基于GIS与信息量模型的地质灾害危险性评价——以云南省丘北县为例[J].西北师范大学学报(自然科学版),2018, 54(1):67-76.TAN S C, ZHAO X Y, LI Y P, et al. Risk assesment on the geological disasters based on GIS and information content model: Taking Qiubei County, Yunnan Province as an example[J]. Journal of Northwest Normal University(Natural Science)2018, 54(1): 67-76.
[3] 胡胜,曹明明,李婷,等.基于AHP和GIS的陕西省地震次生地质灾害危险性评价[J].第四纪研究,2014,34(2):336-345.HU S,CAO M M,LI T,et al. Danger assessment of earthquake induced geological disasters in Shanxi Province based on AHP and GIS[J]. Quaternary Sciences,2014, 34(2):336-345.
[4] 刘丽娜,许冲,徐锡伟,等.GIS支持下基于AHP方法的2013年芦山地震区滑坡危险性评价[J].灾害学,2014, 29(4):183-191.LIU L N, XU C, XU X W, et al. GIS-based landslide hazard evaluation using AHP method in the 2013 Lushan earthquake region[J]. Journal of Catastrophology,2014,29(4):183-191.
[5] 杨俊辉.基于GIS的岩桑树水电站近坝区滑坡风险性评价[D]. 吉林大学,2014.YANG J H. Risk appraisal of landslide adjacent to the dam site of Yansangshu hydropower station based on GIS[D]. Jilin University,2014.
[6] 朱吉祥,张礼中,周小元,等.基于信息熵的灰色模型在地质灾害评价中的应用——以四川青川县为例[J].灾害学,2012, 27(1):78-82,97.ZHU J X, ZHANG L Z, ZHOU X Y, et al. Analysis on decoupling relationship between natural disasters and grain production in China[J]. Journal of Catastrophology,2012, 27(1):78- 82,97.
[7] 刘林通,孟兴民,郭鹏,等.基于流域单元和信息量法的白龙江流域泥石流危险性评价[J].兰州大学学报(自然科学版),2017, 53(3):292-298,30.LIU L T, MENG X M, GUO P, et al. Assesment of debris flow hazards in the Bailongjiang River based on the watershed unit and information value method[J].Journal of Lanzhou University(Natural Science),2017, 53(3): 292-298,30.
[8] 邓辉,何政伟,陈晔,等.信息量模型在山地环境地质灾害危险性评价中的应用——以四川泸定县为例[J].自然灾害学报,2014, 23(2):67-76.DENG H, HE Z W, CHEN Y, et al. Application of information quantity model to hazard evaluation of geo-logical disaster in mountainous region environment:A case study of Luding County,Sichuan Province[J]. Journal of Natural Disasters,2014, 23(2): 67-76.
[9] 许冲,戴福初,徐素宁,等.基于逻辑回归模型的汶川地震滑坡危险性评价与检验[J].水文地质工程地质,2013, 40(3):98-104.XU C, DAI F C, XU S N, et al. Application of logistic regression model on the Wenchuan earthquake triggered landslide hazard mapping and its validation[J]. Hydrogeology and Engineering Geology,2013, 40(3): 98-104.
[10] SOLAIMANI K, MOUSAVI S Z, KAVIAN A. Landslide susceptibility mapping based on frequency ratio and logistic regression models[J]. Arabian Journal of Geosciences,2013, 6(7): 2557-2569.
[11] NEUH?USER B, TERHORST B. GIS-based assessment of landslide susceptibility on the base of the Weights-of-Evidence model[J]. Landslides,2012, 9(4): 511-528.
[12] ILIA I, TSANGARATOS P. Applying weight of evidence method and sensitivity analysis to produce a landslide susceptibility map[J]. Landslides,2016, 13(2): 379-397.
[13] 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 X Q, LAI C G, CHEN X H, 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.
[14] YOUSSEF A M, POURGHASEMI H R, POURTAGHI Z S, et al. Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia[J]. Landslides, 2016, 13 (5): 839-856.
[15] 江强强,方堃,章广成.基于新组合赋权法的地质灾害危险性评价[J].自然灾害学报,2015, 24(3):28-36.JIANG Q Q, FANG K, ZHANG G C. Assessment of geohazards risk based on new combined weight method[J]. Journal of Natural Disasters,2015, 24(3): 28-36.
[16] 贾贵义,全永庆,黎志恒,等.基于组合赋权法的白龙江流域甘肃段地质灾害危险性评价[J].冰川冻土,2014,36(5):1227-1236.JIA G Y,QUAN Y Q,LI Z H,et al. Geo-hazards assessment for the Gansu segment in Bailongjiang River basin by using combination weighting method[J]. Journal of Glaciology & Geocryology,2014,36(5):1227-1236.
[17] GUZZETTI F, CARRARA A, CARDINALI M, et al. Landslide hazard evaluation: A review of current techniques and their application in a multi-scale study, Central Italy[J]. Geomorphology, 1999, 31(1-4): 181-216.
[18] 李军,周成虎.基于栅格GIS滑坡风险评价方法中格网大小选取分析[J].遥感学报,2003, 7(2):86-92,162.LI J,ZHOU C H. Appropriate grid size for terrain based landslide risk assessment in Lantau Island,Hong Kong[J]. Journal of Remote Sensing,2003,7(2):86-92,162.
[19] 乔建平,赵宇.滑坡危险度区划研究述评[J].山地学报,2001,19(2):157-160.QIAO J P,ZHAO Y. Review on rick degree regionalization of landslide[J]. Journal of Mountain Research,2001,19(2):157-160.
[20] YIN H, FUSHENG H U, SUI S, et al. Sensitivity analysis of loess landslide terrain based on statistic and numerical simulation[J]. Journal of Arid Land Resources & Environment, 2010, 24(12): 138-144.
[21] 宿星,魏万鸿,郭万钦,等.基于SRTM DEM的地形起伏度对天水市黄土滑坡的影响分析[J].冰川冻土,2017,39(3):616-622.SU X,WEI W H,GUO W Q,et al. Analyzing the impact of relief amplitude to loess landslides based on SRTM DEM in Tianshui Prefecture[J]. Journal of Glaciology & Geocryology, 2017,39(3):616-622.
[22] HU W,XU Q,WANG G H, et al. Sensitivity of the initiation of debris flow to initial soil moisture[J]. Land-slides,2015,12(6): 1139-1145.
[23] 杨泰平,唐川,齐信.基于GIS技术的汶川8.0级地震诱发地质灾害危险性评价——以四川省安县为例[J].灾害学,2009,24(4):68-72.YANG T P,TANG C,QI X. Evaluation on geological disasters triggered by the 5.12 Wenchuan earthquake based on GIS technology: A case study in Anxian County of Sichuan province[J]. Journal of Catastrophology,2009,24(4):68-72.
[24] 向灵芝,崔鹏,张建强,等.汶川县地震诱发崩滑灾害影响因素的敏感性分析[J].四川大学学报(工程科学版),2010,42(5):105-112.XIANG L Z,CUI P,ZHANG J Q,et al. Triggering factors susceptibility of earthquake-induced collapses and landslides in Wenchuan County[J]. Journal of Sichuan University,2010,42(5):105-112.
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