[1]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.[doi:10.16030/j.cnki.issn.1000 -3665.201906074]
 SUN Changming,MA Runyong,SHANG Hexin,et al.Landslide susceptibility assessment in Xining based on landslide classification[J].Hydrogeology & Engineering Geology,2020,47(3):173-181.[doi:10.16030/j.cnki.issn.1000 -3665.201906074]
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基于滑坡分类的西宁市滑坡易发性评价()
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《水文地质工程地质》[ISSN:1000-3665/CN:11-2202/P]

卷:
47卷
期数:
2020年3期
页码:
173-181
栏目:
环 境 地 质
出版日期:
2020-05-15

文章信息/Info

Title:
Landslide susceptibility assessment in Xining based on landslide classification
文章编号:
1000 -3665(2020)03 -0173 -09
作者:
孙长明1马润勇1尚合欣1谢文波1李焱1刘义2王彪2王思源2
1.长安大学地质工程与测绘学院,陕西 西安710054;2.中国地质调查局水文地质环境地质调查中心,河北 保定071051
Author(s):
SUN Changming1 MA Runyong1 SHANG Hexin1 XIE Wenbo1 LI Yan1 LIU Yi2 WANG Biao2 WANG Siyuan2
1.School of Geological Engineering and Surveying Engineering,Chang’an University,Xi’an,Shaanxi710054,China; 2.Centre for Hydrogeology and Environmental Geology,China Geological Survey,Baoding,Hebei071051,China
关键词:
易发性评价滑坡分类人工神经网络加权信息量模型
Keywords:
landslide susceptibility assessment landslide classification artificial neural network weighted information value model
分类号:
P642.22
DOI:
10.16030/j.cnki.issn.1000 -3665.201906074
摘要:
以往的滑坡易发性评价多以全体滑坡为研究对象,忽视了滑坡类型的区别。各评价指标对不同类型滑坡的影响程度不同,也导致指标权重无法精确地反映其对滑坡的影响。为更准确地对滑坡灾害进行空间预测,针对西宁市滑坡特征及发育机理,将全区滑坡分为土质滑坡和岩质滑坡;在野外实际调查的基础上,结合相关性分析,选取坡度、坡向、剖面曲率、平面曲率、工程地质岩组,以及滑坡点距断层、水系、道路的距离远近等8项因素作为滑坡易发性评价指标,并通过滑坡点分布密度和滑坡点相对分布密度,分析各评价指标分别对土质滑坡和岩质滑坡的影响;利用信息量模型,计算各评价指标对两类滑坡的信息量值,利用人工神经网络模型,赋予各评价指标对两类滑坡的权重;最后基于GIS平台利用加权信息量模型对研究区进行易发性评价。通过统计方法和ROC曲线法分别计算滑坡易发性评价成功率,结果表明:评价成功率可达到82.61%和82.30%,与未经滑坡分类的成功率比较,分别提高了10.9%和5.2%;同时,经过滑坡分类后,湟水河两岸地质条件较差的地区转变为滑坡高易发区。
Abstract:
In the past, all landslides were mostly taken as the research object in the susceptibility evaluation, but the difference in types of landslides was ignored, leading to the fact that the index weight did not accurately reflect its impact on landslides. In order to accurately predict the landslide disaster, predecessors have put forward a variety of evaluation models: expert scoring, logical regression, neural network, and so on. Those studies have promoted the transformation of landslide susceptibility mapping from qualitative to quantitative. On the basis of previous studies, this paper analyses the feature and mechanism of landslides in the city of Xining, and puts forward landslide susceptibility mapping based on landslide classification. Through field investigation, landslides in the whole area are divided into soil landslides and rock landslides. Based on GIS platform, the geological data are extracted as raster data. Finally, the weighted information value is used to evaluate the vulnerability of the study area. Statistic methods and ROC curve are used to calculate the success rate. The results show that after landslide is classified the success rates are 82.61% and 82.30%, increasing respectively by 10.9% and 5.2%. Areas with poor geological conditions on both sides of the Huangshui River are transformed into highly susceptible areas. It is confirmed that landslide susceptibility mapping based on landslide classification is an effective means for landslide susceptibility mapping.

参考文献/References:

[1]王佳佳. 三峡库区万州区滑坡灾害风险评估研究[D].武汉:中国地质大学(武汉),2015.
[WANG J J. Landslide risk assessment in Wanzhou County, Three Gorges Reservior[D]. Wuhan: China University of Geosciences(Wuhan), 2015.(in Chinese)]
[2]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.
[ZHANG J, YIN K L, WANG J J, et al. Evaluation of landslide susceptibility for Wanzhou district of Three Gorges Reservior[J]. Chinese Journal of Rock Mechanics and Engineering, 2016,35(2):284-296.(in Chinese)]
[3]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 J J, YIN K L, XIAO L L. Landslide susceptibility assessment based on GIS and weighted information value: a case study of Wanzhou district, Three Gorges Reservior[J]. Chinese Journal of Rock Mechanics and Engineering, 2014,33(4):797-808.(in Chinese)]
[4]郭子正,殷坤龙,付圣,等.基于GIS与WOE -BP模型的滑坡易发性评价[J].地球科学,2019,44(12):4299-4312.
[GUO Z Z, YIN K L, FU S, et al. Evaluation of landslide susceptibility based on GIS and WOE -BP model[J]. Earth Science, 2019,44(12):4299-4312.(in Chinese)]
[5]ERENER A, MUTLU A, SEBNEM DüZGüN H. A comparative study for landslide susceptibility mapping using GIS -based multi -criteria decision analysis (MCDA), logistic regression (LR) and association rule mining (ARM)[J]. Engineering Geology, 2016(203):45-55.
[6]郭子正,殷坤龙,黄发明,等.基于滑坡分类和加权频率比模型的滑坡易发性评价[J].岩石力学与工程学报,2019,38(2):287-300.
[GUO Z Z, YIN K L, HUANG F M, et al. Evaluation of landslide susceptibility based on landslide classification and weighted frequency ratio model[J]. Chinese Journal of Rock Mechanics and Engineering, 2019,38(2):287-300.(in Chinese)]
[7]KAVZOGLU T, KUTLUG SAHIN E, COLKESEN I. Selecting optimal conditioning factors in shallow translational landslide susceptibility mapping using genetic algorithm[J]. Engineering Geology, 2015(192):101-112.
[8]冯杭建,周爱国,俞剑君,等.浙西梅雨滑坡易发性评价模型对比[J].地球科学,2016,41(3):403-415.
[FENG H J, ZHOU A G, YU J J, et al. A comparative study on plum -rain -triggered landslide susceptibility assessment models in west Zhejiang Province[J]. Earth Science, 2016,41(3):403-415.(in Chinese)]
[9]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.
[10]王卫东,陈燕平,钟晟.应用CF和Logistic回归模型编制滑坡危险性区划图[J].中南大学学报(自然科学版),2009,40(4):1127-1132.
[WANG W D, CHEN Y P, ZHONG S. Landslide susceptibility mapped with CF and logistic regression model[J]. Journal of Central South University (Science and Technology), 2009,40(4):1127-1132.(in Chinese)]
[11]ZHOU C, YIN K, CAO Y, et al. Landslide susceptibility modeling applying machine learning methods: A case study from Longju in the Three Gorges Reservoir area, China[J]. Computers & Geosciences, 2018,112:23-37.
[12]CHEN W, PENG J B, HONG H Y, et al. Landslide susceptibility modelling using GIS -based machine learning techniques for Chongren County, Jiangxi Province, China[J]. Science of the Total Environment, 2018, 626:1121-1135.
[13]王森,许强,罗博宇,等.基于分形理论的南江县滑坡敏感性分析与易发性评价[J].水文地质工程地质,2017, 44(3):119-126.[
WANG S, XU Q, LUO B Y, et al. Vulnerability analysis and susceptibility evaluation of landslides based on fractal theory in Nanjiang County[J]. Hydrogeology & Engineering Geology, 2017,44(3): 119-126.(in Chinese)]
[14]唐睿旋,晏鄂川,唐薇.基于粗糙集和BP神经网络的滑坡易发性评价[J].煤田地质与勘探,2017,45(6): 129-138.
[TANG R X, YAN E C, TANG W. Landslide susceptibility evaluation based on rough set and back -propagation neural network[J]. Goal Geology & Exploration,2017,45(6):129-138.(in Chinese)]
[15]彭令. 三峡库区滑坡灾害风险评估研究[D]. 武汉:中国地质大学(武汉),2013.
[PENG L. Landslide risk assessment in the Three Gorges Reservior[D]. Wuhan: China University of Geosciences(Wuhan),2013.(in Chinese)]
[16]杨玲,权开兄,代庆礼,等.西宁市重大地质灾害隐患分布规律研究[J].青海环境,2015,25(3):113-116.
[YANG L, QUAN K X, DAI Q L, et al. Study on the distribution law of major geological hazards in Xining[J]. Journal of Qinghai Environment, 2015,25(3):113-116.(in Chinese)]
[17]周保,张俐,王文采,等.青海省西宁市地质灾害详细调查报告[R].西宁:青海省地质环境监测院,2013.
[18]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):165-172.
[REN J, FAN X M, ZHAO C, et al. Evaluation of the landslide vulnerability in Duyun of Guizhou Province[J]. Hydrogeology & Engineering Geology, 2018,45(5): 165-172.(in Chinese)]
[19]范林峰,胡瑞林,曾逢春,等.加权信息量模型在滑坡易发性评价中的应用——以湖北省恩施市为例[J].工程地质学报,2012,20(4):508-513.[FAN L F, HU R L, ZENG F C, et al. Application of weighted information value model to landslide susceptibility assessment—a case study of Enshi city, Hubei province[J]. Journal of Engineering Geology, 2012,20(4):508-513.(in Chinese)]
[20]刘艺梁,殷坤龙,刘斌.逻辑回归和人工神经网络模型在滑坡灾害空间预测中的应用[J].水文地质工程地质,2010,37(5):92-96.
[LIU Y L, YIN K L, LIU B. Application of logistic regression and artificial neural networks in spatial assessment of landslide hazards[J]. Hydrogeology & Engineering Geology, 2010,37(5): 92-96.(in Chinese)]
[21]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.
[22]PRADHAN B, LEE S. Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling[J]. Environmental Modelling & Software, 2010,25(6):747-759.
[23]向喜琼. 区域滑坡地质灾害危险性评价与风险管理[D]. 成都:成都理工大学,2005.
[XIANG X Q. Regional landslide hazard assessment and risk management[D]. Chengdu: Chengdu University of Technology, 2005.(in Chinese)]
[24]冯杭建,周爱国,唐小明,等.基于确定性系数的降雨型滑坡影响因子敏感性分析[J].工程地质学报,2017,25(2):436-446.
[FENG H J, ZHOU A G, TANG X M, et al. Susceptibility analysis of factors controlling rainfall -triggered landslide using certainty factor method[J]. Journal of Engineering Geology, 2017,25(2):436-446.(in Chinese)]
[25]YESILNACAR E, TOPAL T. Landslide susceptibility mapping: a comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey)[J]. Engineering Geology, 2005, 79(3/4):251-266.
[26]刘艺梁,殷坤龙,刘斌,等.逻辑回归和人工神经网络模型在滑坡灾害空间预测中的应用[J].水文地质工程地质,2010,37(5):92-96.
[LIU Y L, YIN K L, LIU B, et al. Application of logistic regression and artificial neural networks in spatial assessment of landslide hazards[J]. Hydrogeology & Engineering Geology, 2010,37(5): 92-96.(in Chinese)]

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 WANG Sen,XU Qiang,LUO Boyu,et al.Vulnerability analysis and susceptibility evaluation of landslides based on fractal theory in Nanjiang County[J].Hydrogeology & Engineering Geology,2017,44(3):119.
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备注/Memo

备注/Memo:
收稿日期: 2019 -06 -29; 修订日期: 2019 -10 -13
基金项目: 中国地质调查局地质调查项目(DD20160262;DD20190268)
第一作者: 孙长明(1994 -),男,硕士研究生,地质工程专业。E -mail:suncm1994@163.com
通讯作者: 马润勇(1961 -),男,教授,博士,主要从事地质工程、岩土工程等方面的研究。E -mail:13572091368@163.com
更新日期/Last Update: 2020-05-15