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基于GDIV模型的大渡河中游地区滑坡危险性评价与区划

阳清青, 余秋兵, 张廷斌, 易桂花, 张恺

阳清青,余秋兵,张廷斌,等. 基于GDIV模型的大渡河中游地区滑坡危险性评价与区划[J]. 中国地质灾害与防治学报,2023,34(5): 130-140. DOI: 10.16031/j.cnki.issn.1003-8035.202208014
引用本文: 阳清青,余秋兵,张廷斌,等. 基于GDIV模型的大渡河中游地区滑坡危险性评价与区划[J]. 中国地质灾害与防治学报,2023,34(5): 130-140. DOI: 10.16031/j.cnki.issn.1003-8035.202208014
YANG Qingqing,YU Qiubing,ZHANG Tingbin,et al. Landslide hazard assessment in the middle reach area of the Dadu River based on the GDIV model[J]. The Chinese Journal of Geological Hazard and Control,2023,34(5): 130-140. DOI: 10.16031/j.cnki.issn.1003-8035.202208014
Citation: YANG Qingqing,YU Qiubing,ZHANG Tingbin,et al. Landslide hazard assessment in the middle reach area of the Dadu River based on the GDIV model[J]. The Chinese Journal of Geological Hazard and Control,2023,34(5): 130-140. DOI: 10.16031/j.cnki.issn.1003-8035.202208014

基于GDIV模型的大渡河中游地区滑坡危险性评价与区划

基金项目: 国家自然科学基金项目(41801099)
详细信息
    作者简介:

    阳清青(1997-),女,四川南充人,硕士研究生,主要从事环境遥感研究。E-mail:2020050063@stu.cdut.edu.cn

    通讯作者:

    余秋兵(1989-),男,四川南充人,硕士,工程师,主要从事地质构造与地质调查研究工作。E-mail:yu8ye4@yeah.net

  • 中图分类号: P642.22

Landslide hazard assessment in the middle reach area of the Dadu River based on the GDIV model

  • 摘要: 区域地质灾害评价是减灾防治的重要非工程手段,构建区域滑坡危险性评价模型,对提高地质灾害评价精度和防治效率具有重要意义。文章以滑坡频发的大渡河中游地区为研究区,初选高程、坡度、坡向、地震动参数、土壤类型、工程地质岩组、年平均降雨量和地形湿度指数(TWI)等13个因子,建立滑坡危险性初级评价指标体系。考虑各因子对滑坡形成贡献程度的不同和目前常权栅格叠加方式对滑坡危险性评价结果精度的影响,引入了地理探测器和变权栅格叠加,构建了地理探测器、信息量法和变权栅格叠加的组合模型(GDIV模型)。基于2021年四川省1∶50 000地质灾害风险调查中313处滑坡地质灾害隐患点,开展基于GDIV模型的大渡河中游地区滑坡危险性评价,并与逻辑回归模型和信息量模型的组合模型(LRI模型)评价结果进行对比分析。结果表明:研究区以中危险及以下危险区为主,占总面积的78.3%,极高和高危险区主要分布在大渡河、革什扎河和东谷河两岸的低海拔地区;与LRI模型相比,基于GDIV模型的评价结果精度更高,其受试者工作特征(ROC)曲线的线下面积(AUC)值为0.917。文章提出的GDIV模型提高了区域滑坡危险性评价精度,可为类似地区地质灾害评价提供方法参考。
    Abstract: Regional geological hazard assessment is an important non-engineering approach for disaster reduction and prevention. Constructing a regional landslide hazard assessment model is of great significance in improving the accuracy of geological hazard evaluation and the efficiency of prevention. This study focuses on the frequent landslide occurrence in the middle reach area of the Dadu River and selects 13 primary factors, including elevation, slope, aspect, seismic parameters, soil type, engineering geological lithology, annual average rainfall, and topographic wetness index (TWI), to establish a primary evaluation index system for landslide hazard. Considering the varying contributions of each factor to landslide formation and the impact of the commonly used weighted raster superposition methods on assessment accuracy, the geographic detector and variable weight raster overlay techniques are introduced, leading to the development of the GDIV model. Using data from 313 landslide hazard points identified in the 2021 geological hazard risk survey at a scale of 1∶50,000 in Sichuan Province, the landslide hazard assessment in the middle reach area of the Dadu River basin is conducted based on the GDIV model, and the evaluation results are compared with those of the LRI model. The results show that the study area is predominantly characterized by middle and lower risk areas, accounting for 78.3% of the total area. The extremely high and high-risk areas are primarily located in the low-elevation regions along the banks of Dadu River, Geshizha River, and Donggu River. Compared to the LRI model, the evaluation results based on the GDIV model exhibit higher accuracy, with an area under the receiver operating characteristics (ROC) curve of 0.917. The GDIV model proposed in this paper improves the accuracy of regional Landslide hazards assessment, and serves as a valuable reference for similar geological disaster evaluations in other areas.
  • 我国西南部山区滑坡自然灾害频发,严重影响城市发展。滑坡是一种斜坡岩土体,沿地层间形成的软弱结构面滑移的现象,物理力学性质表现为抗滑力小于下滑力,具有瞬时性、强破坏性等特点。滑坡易发性分区可为滑坡防治工程提供指导建议及勘察方向。

    滑坡易发性评价是预测滑坡灾害分布情况并提供防治建议的手段之一[1]。滑坡分布主要受地质环境条件、气象条件、人类工程活动等因素的影响[2]。目前,滑坡易发性评价研究方法主要有单一模型:层次分析法[3]、信息量[45]、频率比[6]、确定性系数[68]、熵指数法[910]、组合赋权[11]、证据权[12]、逻辑回归[67, 9]、神经网络[13]、随机森林[14]、支持向量机[15]等;耦合模型:单一模型间耦合[6 - 7, 9 - 11]。单一模型局限于机械的叠加评价因子,未考虑评价因子的权重差异及滑坡发育的实际条件,而耦合模型则使评价因子以线性关系描述滑坡灾害与孕灾因子间联系。其中确定性系数法属于概率函数模型,可反映滑坡点与孕灾因子的敏感性,定量描述评价单元易发程度,但确定性系数法缺乏考虑评价因子间内在联系及不同孕灾因子对滑坡发育的重要性[7],而层次分析法与熵值法分别从经验认知与数据客观规律上描述评价因子间的内在联系,并根据滑坡点与评价因子的关系确定评价因子权重,由于层次分析法与熵指数法权重计算的两极化(过度经验化、过度遵循数据规律),对此引入距离函数法,它使得同一个评价因子不同权重归于一个中立权重值,即解决权重值两极化问题,又保证前人研究对滑坡经验认识,且不失客观规律。

    因此,本文以确定性系数法为基础,构建了确定性系数法(certainty factor,CF)、确定性系数法与层次分析法(certainty factor-analystic hierarchy process,CF-AHP)、确定性系数法与熵指数法(certainty factor-index of entropy,CF-IOE)及确定性系数法与距离函数法-组合权重(CF-AHP-IOE)的不同滑坡易发性评价模型,并对4种滑坡易发性评价模型结果进行对比分析。论证了耦合模型可解决单一模型数据量少与精度较低的问题,CF-AHP-IOE模型从主、客观角度优化评价指标因子权重值,提高模型评价精度。评价结果为保山盆地周边滑坡灾害的防灾减灾提供参考,研究成果可用于其他滑坡易发性分区中。

    确定性系数法[68, 14]是一种条件概率函数,假设将来与过去发生滑坡灾害的条件相同,表达式为:

    CF={PPaPPsPPs(1PPa)PPa<PPsPPaPPsPPa(1PPs)PPaPPs (1)

    式中:PPa——滑坡灾害评价因子a类分级发生的条件 概率,即a类分级滑坡数量与a类分级 面积之比;

    PPs——滑坡灾害在整个研究区中发生的先验概 率,即研究区域滑坡总数与总面积之比;

    CF——确定系数值,范围在[−1, 1]内,表征评价因 子对滑坡发生的确定性系数,正值代表滑 坡发生的确定性大,负值代表滑坡发生的 确定性小,0代表不能确定对滑坡发育影 响状态。

    AHP[3]是一种多目标决策问题的方法。将目标层次化,按因素的相关性构建与目标层的有序递进层次结构,核心在于判断各层因素与上层隶属关系。据经验法与前人成果,对因素进行两两比较并标度赋值,构建判断矩阵并检验其一致性。

    以式(1)计算的评价因子确定性系数值为基础,采用层次分析计算评价因子权重值,并加权赋值计算单元栅格总确定性系数值(CF),即滑坡易发性程度,表达式为:

    CF=i=1nCFij(CFij,WiAHP) (2)

    式中:Wi-AHP——第i个评价指标因子权重;

    CFij(CFij, Wi-AHP )——第i个评价因子中j类分级的 加权确定性系数值。

    IOE[9 - 10]模型属于客观权重方法之一,熵代表系统的不确定性,滑坡评价指标因子的能量熵反映各类影响因子对滑坡的重要性,即可依据频率比值估算评价指标因子权重值,具有样本数据要求少,客观性强等优势。其计算步骤如下:

    Fij=ZS (3)
    (Fij)=Fij/j=1NFij,j=1,,N (4)
    Hi=j=1N(Fij)log2(Fij)i=1,,n (5)
    Himax=log2N (6)
    Pi=(HimaxHi)/Himax (7)
    Fi=j=1NFij/N (8)
    Wi=PiFi (9)
    WiIOE=Wi/inWi (10)

    式中: Fij——各评价因子分级的频率比值;

    SZ——对应分级的面积百分比、滑坡灾害百分比;

    (Fij)——概率密度;

    HiHimax——熵值;

    N——评价指标因子分级数;

    n——评价指标因子个数;

    Pi——评价因子信息率;

    Wi——评价因子权重。

    Wi进行归一化,得到WiIOE,即评价指标因子权重系数。

    由于AHP主观性强,精度受专家经验影响较大,因此引入IOE,计算客观权重值。采用距离函数法[11],以主、客观权重求组合权重值(WiAHPIOE),旨在于既考虑研究人员对滑坡发育认识,又不失客观规律。计算步骤如下:

    假设WiAHPIOE为其组合权重值,是其W1W2为主、客观权重值线性相加,式子如下:

    WiAHPIOE=αW1+βW2 (11)

    式中:WiAHPIOE——组合权重值;

    W1W2——主、客观权重值,分别为WiAHPWiIOE

    由于层次分析法与熵指数法计算的权重值具有一定差异性,为消除这种差异性,提高模型评价精度,由此引入距离函数d(W1,W2),其表达式为:

    d(W1,W2)=[12i=1n(W1W2)2]12i=1,2,,n (12)

    为使评价因子主、客观权重值的差异性与分配系数保持一致性,对距离函数与分配系数取等,其计算方式如下:

    d(W1,W2)2=(αβ)2 (13)
    α+β=1 (14)

    式中:αβ——主、客观分配系数,其和等于1是分配 系数约束条件;

    滑坡易发性评价精度检验通常使用受试者工作特征曲线(receiver operating characteristic curve,ROC)的AUC(area under curve)值度量[7, 16]。ROC 曲线是一种检测自变量对因变量是否敏感,滑坡易发性值为检验自变量,滑坡是否发生为二分类因变量。本文以面积比法作为ROC 曲线绘制方法,曲线横坐标为1-特异性(假正例率),即易发性分区面积由极高到低易发区的百分比累加;纵坐标为敏感度(真正例率),即相应易发区面积的实际滑坡栅格面积百分比累加。 ROC曲线下方面积为模型评价精度,即AUC值,大于0.7模型具有较高评价精度,越接近1滑坡易发分区越准确。

    研究区地处云南省西部,保山市东北部。总面积约765 km2,保山盆地面积约213 km2,占比28%,地势西北高、东南低。研究区地质构造复杂,断裂纵横交错,褶皱多数形态不完整,发育呈 “歹”字型断裂带,主要有卧佛寺断裂、清水沟断裂、李家寺断裂等。出露较多的为第四系(Q)黏土、含砾石黏土、砂砾石层、黏土夹泥炭多层土体;三叠系(T)黏土、灰岩、玄武岩、白云岩;二叠系(P)灰岩、白云质灰岩;石炭系(C)页岩夹泥质灰岩、玄武岩;泥盆系(D)泥质灰岩及灰质泥岩;志留系(S)页岩、粉砂岩及泥质灰岩;奥陶系(O)页岩、粉砂岩、泥岩;寒武系(Є)灰岩、砂岩、白云岩、泥质灰岩等地层。受人类工程活动、地质条件等影响该地区的滑坡灾害发育,据野外调查与遥感解译研究分析得出滑坡99个。研究区地理位置及滑坡灾害分布,如图12所示。

    图  1  研究区地理位置
    Figure  1.  Geographic location of the study area
    图  2  研究区滑坡灾害分布
    Figure  2.  Distribution of landslide hazards in the study area

    通过分析区域地质环境及灾害发育特征,选取海拔、坡度、坡向、归一化植被覆盖度(normalized vegetation index,NDVI)、工程地质岩组、距道路距离、距断层距离、距水系距离、灾害点密度,共计9个评价因子,对研究区进行滑坡易发性评价。

    为保证确定性系数法模型评价精度,对评价指标因子进行相互独立性检验。利用ArcGIS波段统计工具计算评价指标因子的相关性系数矩阵(表1)。结果表明:海拔与坡度、NDVI、道路,坡度与NDVI、水系呈现低度相关(0.3<相关系数值<0.5),可能对模型评价精度产生影响。

    表  1  评价指标因子相关系数矩阵
    Table  1.  Matrix of correlation coefficients of evaluation indicator factors
    评价因子 海拔 坡度 坡向 NDVI 工程地质岩组 道路 断层 水系 灾害点密度
    海拔 1 0.471 −0.017 0.412 0.272 0.325 −0.100 −0.162 −0.132
    坡度 1 −0.001 0.402 0.269 0.070 −0.188 −0.382 0.008
    坡向 1 −0.088 0.127 −0.025 −0.042 0.062 0.020
    NDVI 1 0.203 0.159 −0.150 −0.264 −0.018
    工程地质岩组 1 −0.061 −0.259 −0.252 0.176
    道路 1 0.030 0.162 −0.146
    断层 1 0.227 −0.156
    水系 1 −0.126
    灾害点密度 1
    下载: 导出CSV 
    | 显示表格

    为确保模型评价精度,据层次分析法计算评价因子权重顺序,如:灾害点密度>工程地质岩组>坡度>断层>道路>水系>NDVI >坡向>海拔(表2)。结合评价指标因子相关性及重要性(即:研究人员对滑坡发育的经验认识,得出的评价因子重要程度),依次剔除海拔、NDVI、水系,分别建立评价模型。结果表明:未剔除评价指标因子的AUC值0.890,模型评价精度最高(表3)。因此9个评价因子均可纳入模型的构建。

    表  2  评价因子判断矩阵及权重值
    Table  2.  Judgment matrix and weight values of evaluation factors
    评价因子 海拔 坡度 坡向 归一化植被覆盖度 工程地质岩组 道路 断层 水系 灾害点密度 Wi
    海拔 1 1/7 1/2 1/3 1/6 1/4 1/5 1/2 1/8 0.024
    坡度 1 5 3 1/3 1 1 2 1/4 0.115
    坡向 1 1 1/6 1/2 1/3 1/2 1/8 0.036
    归一化植被覆盖度 1 1/5 1 1/2 1 1/6 0.052
    工程地质岩组 1 4 2 3 1/3 0.202
    道路 1 1/2 1 1/5 0.068
    断层 1 2 1/4 0.106
    水系 1 1/5 0.058
    灾害点密度 1 0.339
    下载: 导出CSV 
    | 显示表格
    表  3  不同模型AUC
    Table  3.  AUC values of different models
    剔除因子 未剔除 海拔 海拔、NDVI 海拔、NDVI、水系
    AUC 0.890 0.875 0.871 0.871
    下载: 导出CSV 
    | 显示表格

    评价指标因子分级是统计分析结果合理性的基础。海拔以自然断点法进行分级;坡度、坡向、归一化植被覆盖度(NDVI)以等距分级;离散型评价因子有工程地质岩组、距道路距离、距断层距离、距水系距离、灾害点密度,据滑坡发育特征及现场调查进行分级;工程地质岩组据岩体建造、岩性组合、岩体结构类型、岩石力学性质分级。

    频率比法(frequency ratio,FR)[6 - 7]是一种基于统计学的预测方法,FR值表征各级评价指标因子对滑坡灾害发生的贡献度,FR>1即有利于滑坡灾害的发生,值越大贡献度越大。因此以FR值对各级评价指标因子进行分析(图34)。

    图  3  评价指标因子分级占比、灾害占比及频率比值趋势图
    Figure  3.  Trend charts of graded proportion of evaluation indicator factors, disaster proportions and frequency ratios
    图  4  滑坡灾害评价指标因子
    Figure  4.  Evaluation indicator factors for landslide hazard

    海拔高度不同,植被密度与人类活动存在较大差异,间接影响斜坡稳定及滑体物源分布情况,影响滑坡发育[3]。研究区海拔在1582~3 098 m,其中1724~2 167 m内FR值大于1,易于滑坡的发生。

    坡度反映了地形倾斜程度,大小决定表面径流量、坡面冲刷速率及体土层剩余下滑力,影响坡体稳定性[7]。研究区最大坡度近59°,以8°为等距分级,大于40°为一级,在坡度大于8°的斜坡体FR值均大于1,有利于滑坡发生。

    坡向间接影响斜坡曝光度、风化程度、降雨强度及土壤湿度[17]。研究区山脉南北展布,东、南、西南、西方向上,FR值为1.150、1.153、1.441、1.418,促进滑坡发育。

    归一化植被覆盖度[7, 18]范围在[0, 1],以Landsat 8 遥感影像数据为基础,据像元二分模型计算NDVI值:

    NDVI=(NIRR)/(NIR+R) (15)

    式中:NDVI——归一化植被覆盖度;

    NIR——遥感影像中近红外波段;

    R——红光波段。

    归一化植被覆盖度数值越大栅格单元植被覆盖度越高。因此以0.2为间隔分级,在0.4~0.8区间,FR值为1.526、1.510为滑坡集中发生范围。

    工程地质岩组[17]为滑坡物源基础,受降雨、地壳运动及风化剥蚀等影响,控制滑坡类型及分布情况。将研究区岩土体分为松散土体、碎屑岩岩组、碳酸盐岩岩组、碳酸盐岩夹碎屑岩岩组、变质岩岩组、岩浆岩岩组共6类岩组。其中碳酸盐岩岩组、碳酸盐岩夹碎屑岩岩组、岩浆岩岩组岩性为页岩、泥质灰岩、灰岩夹粉砂岩、泥岩,FR值均大于1,对滑坡发育有利。

    道路是人类活动强度的体现,道路修建过程中坡脚开挖、植被破坏,改变岩土体原有的环境条件,破坏斜坡体稳定性[6, 17]。以300 m为等距制作道路缓冲区,在距道路600 m以内,FR值为2.109、1.655,距道路越近坡体稳定性越差,滑坡易形成。

    断裂构造产生了岩石破碎带,物理力学性质变差,风化剥蚀加剧,地层错位形成软弱带[6, 8, 17]。以300 m为等距制作断层缓冲区,在距道路1 200 m以内,FR值均大于1,严重影响岩石物理力学性质,距断裂带越近,越促进滑坡形成。

    水系反映了水对斜坡底部的侵蚀及软化作用,易形成临空面,破坏斜坡的稳定性[19]。以水文分析法提取水系,将水系以200 m等距制作缓冲区,将研究区划分5级,在距水系400 m以内,FR值为2.323、1.144,越近滑坡越发育。

    滑坡灾害点密度[2, 1922]反映流域滑坡灾害发育情况,表现为滑坡群发效应,不同岩组、斜坡体结构及人类活动强度的不均匀分布,导致流域内滑坡易发程度差异,是灾害发生的结果。但由于研究区盆地的面积占比28%,滑坡灾害主要发育在山谷内,且流域内影响滑坡发育的主要因子不同及滑坡失稳后造成区域地质不稳定性,小型群发性滑坡常集中在流域内(图5),由于勘察局限性,忽略极小型滑坡灾害,因此引入流域内灾害点密度作为评价因子,弥补评价因子不准确造成的误差,提高模型精度。以水文分析法划分研究区流域单元,滑坡点密度为流域内的灾害点个数与流域面积之比,研究区灾害点密度在0~2.11个/km2,分为0,0~1,1~2.11个/km2,在1~2.11个/km2的区间FR值最大为6.039。

    图  5  流域单元滑坡灾害图
    Figure  5.  Landslide hazard map for watershed units

    据式(1),计算各评价指标因子分级的确定性系数值(CF)(表4),采用地理信息系统(geographic information system,GIS)对评价指标因子层进行叠加,利用自然断点法将叠加图层进行分区,即得出CF模型的滑坡易发性分区图。

    表  4  评价因子确定性系数值及权重值
    Table  4.  Coefficient of determination values and weight values for evaluation factors
    评价因子 状态分级 CF WiAHP WiIOE WiAHPIOE 评价因子 状态分级 CF WiAHP WiIOE WiAHPIOE
    海拔/m 15821724 −0.357 0.024 0.116 0.067 0.8~1 −0.827
    17241960 0.658 工程地质岩组 松散土体 −0.282 0.202 0.042 0.126
    1 960~2167 0.280 碎屑岩岩组 −0.260
    21672394 −0.463 碳酸盐岩岩组 0.122
    23942644 −1.000 碳酸盐岩夹碎屑岩组 0.605
    26443098 −1.000 变质岩岩组 −0.127
    坡度/(°) 0~8 −0.765 0.115 0.026 0.073 岩浆岩岩组 0.687
    8~16 0.374 距道路距离/m 0~300 0.601 0.068 0.035 0.052
    16~24 0.336 300~600 0.453
    24~32 0.273 600~900 −0.215
    32~40 0.007 900~1200 0.115
    >40 0.332 >1200 −0.443
    坡向 平面 −1.000 0.036 0.031 0.034 距断层距离/m 0~300 0.437 0.106 0.021 0.066
    −0.420 300~600 0.471
    东北 −0.228 600~900 0.451
    0.149 900~1200 0.299
    东南 −0.448 >1200 −0.439
    0.152 距水系距离/m 0~200 0.651 0.058 0.066 0.062
    西南 0.350 200~400 0.144
    西 0.337 400~600 −0.179
    西北 −0.096 600~800 −0.678
    归一化植被
    覆盖度
    0~0.2 −1.000 0.052 0.092 0.071 >800 −0.634
    0.2~0.4 −0.312 灾害点密度/km2 0 −1.000 0.339 0.572 0.449
    0.4~0.6 0.394 0~1 0.808
    0.6~0.8 0.386 1~2.11 0.958
    下载: 导出CSV 
    | 显示表格

    (1)CF-AHP模型

    CF-AHP模型权重信息获取是基于层次分析法构建评价指标体系层次结构模型,计算评价指标因子权重值。根据研究区滑坡发育特征、专家意见及前人成果,采用标度法构建判断矩阵(表3);经一致性检验,CR值为0.028,小于0.1,即权重值有效(表3)。

    以确定性系数值(CF)为基础,据层次分析法计算权重为系数,据式(2)对评价指标因子进行叠加,如式(16),计算栅格单元加权确定系数值,采用自然断点法对加权确定性系数分级,得出CF-AHP耦合模型滑坡易发性分区图。

    (2)CF-IOE模型

    以确定性系数值(CF)为基础,据式(3)—(10),计算评价指标因子权重值,计算栅格单元加权确定系数值,如式(17),采用自然断点法对加权确定性系数分级,得出CF-IOE耦合模型滑坡易发性分区图。

    (3)CF-AHP-IOE模型

    基于确定性系数值(CF),据式(12)—(14),利用距离函数法,计算αβ主、客观分配系数分别为0.527、0.473,据式(11)计算评价指标因子组合权重值,计算栅格单元加权确定性系数值,如式(18),采用自然断点法对加权确定性系数分级,得出CF-AHP-IOE耦合模型滑坡易发性分区图。

    CF-AHP=0.024CF1j+0.115CF2j+0.036CF3j+0.052CF4j+0.202CF5j+0.068CF6j+0.106CF7j+0.058CF8j+0.339CF9j (16)
    CF-IOE=0.116CF1j+0.026CF2j+0.031CF3j+0.092CF4j+0.042CF5j+0.035CF6j+0.035CF7j+0.066CF8j+0.572CF9j (17)
    CF-AHP-IOE=0.067CF1j+0.073CF2j+0.034CF3j+0.071CF4j+0.126CF5j+0.066CF6j+0.062CF7j+0.062CF8j+0.449CF9j (18)

    式中:CF-AHPCF-IOECF-AHP-IOE——CF-AHP、 CF-IOE、 CF-AHP-IOE模型的总加权确定性系数值;

    CF1jCF9j——海拔、坡度、坡向、归一化植被覆 盖度、工程地质岩组、距道路距 离、距断层距离、距水系距离、灾 害点密度的确定性系数值。

    将评价指标因子栅格图层按权重系数(表4)加权计算,采用自然断点法将其划分为低、中、高及极高易发区,得出确定性系数模型(CF)及确定性系数法与层次分析法耦合模型(CF-AHP)、确定性系数法与熵指数法耦合模型(CF-IOE)及确定性系数法与距离函数法-组合权重模型(CF-AHP-IOE)的研究区滑坡易发性分区图(图6)。

    图  6  滑坡易发性分区结果
    Figure  6.  Geological hazard susceptibility zoning results

    以分级面积实际发生的滑坡灾害数量占比、灾害密度、频率比值占比对滑坡评价结果进行分析(表5),结果显示:

    表  5  CF、CF-AHP、CF-IOE、CF-AHP-IOE模型分级结果
    Table  5.  Grading results of CF, CF-AHP 、CF-IOE、CF-AHP-IOE models
    评价模型 易发分区 分级面积/km2 面积占比/% 灾害频数 灾害占比/% 灾害密度/km2 频率比值 频率比值占比/%
    CF 241.328 31.6 1 1.0 0.004 0.032 0.53
    208.166 27.2 5 5.1 0.024 0.186 3.10
    198.697 26.0 14 14.1 0.070 0.544 9.07
    极高 116.628 15.2 79 79.8 0.677 5.233 87.29
    CF-AHP 365.938 47.8 0 0 0 0 0
    197.212 25.8 1 1.0 0.005 0.039 0.50
    107.775 14.1 22 22.2 0.204 1.577 20.04
    极高 93.894 12.3 76 76.8 0.809 6.253 79.46
    CF-IOE 371.350 48.6 0 0 0 0 0
    201.359 26.3 1 1.0 0.005 0.038 0.51
    86.153 11.3 15 15.2 0.174 1.345 18.09
    极高 105.957 13.9 83 83.8 0.783 6.052 81.40
    CF-AHP-IOE 300.304 39.3 0 0 0 0 0
    202.226 26.4 0 0 0 0 0
    130.706 17.1 9 9.1 0.069 0.532 9.15
    极高 131.584 17.2 90 90.9 0.684 5.284 90.85
    下载: 导出CSV 
    | 显示表格

    (1)单一模型,CF模型滑坡易性发分区(低、中、高、极高)面积为241.328,208.166,198.697,116.628 km2,落入极高的滑坡数量占比79.8%,极高易发区的灾害密度0.677个/km2;低—极高易发区的频率比值显著增大,极高易发区频率比值大于1,表明CF模型对研究区进行有效分级。

    (2)耦合模型:CF-AHP、CF-IOE、CF-AHP-IOE模型滑坡易发性分区中的极高易发性分级面积为93.894,105.957,131.584 km2,落入极高易发区的滑坡数量占比76.8%、83.8%、90.9%,灾害密度0.809,0.783,0.684个/km2,频率比值占总频率比值均在79%以上;CF-AHP-IOE极高易发区频率比值占比高达90%;3种模型滑坡易发性分级的灾害频数及密度由低—极高依次增大,符合滑坡发育规律,即3种耦合模型均对研究区滑坡易发性进行了有效评价。

    (3)据CF-AHP、CF-IOE、CF-AHP-IOE模型耦合模型的滑坡易发性评价结果分析:极高易发区的灾害占比及频率比值占比均依次增大;高易发区却依次减小,如表5所示,反映了滑坡向极高易发区集中;极高易发区灾害密度分别为0.809,0.783,0.684个/km2,呈现逐步降低趋势,由于灾害密度受分区面积与灾害频数的影响,追溯到评价指标因子栅格精度及灾害数量多少的问题,将直接影响分区灾害密度值大小,未能体现CF-AHP-IOE模型的优势,但仍然反应极高易发区灾害密度的绝对高占比。而灾害占比及频率比值占比均为无量纲值,可更加反映滑坡灾害的集中程度。

    采用ROC曲线的AUC值检验模型预测精度,曲线横坐标为1-特异性;纵坐标为敏感度,CF、CF-AHP、CF-IOE及CF-AHP-IOE模型AUC值分别为0.890、0.911、0.921、0.916(图7),AUC均大于0.7,均具有较高的准确性,确定性系数值在层次分析法、熵指数法及组合权重法权重系数加权后,评价精度更高,更准确对研究区进行滑坡易发性分级。

    图  7  ROC曲线
    Figure  7.  ROC curve

    (1)评价结果显示:高、极高易发区分布于地势起伏大、中度植被覆盖、断裂构造发育的岩石破碎及人类活动强烈的西南与东北的山区沟谷,表明在人类工程活动与水文条件影响下,坡脚侵蚀软化、坡体稳定性差,极易引发滑坡地质灾害。

    (2)提出基于AHP与ROC的评价指标体系的构建方法。结果表明:结合评价因子间相关性与重要性,取AUC值为0.890的评价模型,模型评价分级结果更好,为评价因子间的弱相关性提供一种可行的解决方法。

    (3)CF、CF-AHP、CF-IOE及CF-AHP-IOE模型滑坡易发性分区的频率比值从低易发区到极高易发区显著增大,极高易发区频率比值占比均在79%以上,具有良好的分级效果。

    (4)CF、CF-AHP、CF-IOE及CF-AHP-IOE模型AUC值分别为0.890、0.911、0.921、0.916,模型评价精度高。采用AHP、IOE及组合权重法进行滑坡易发性评价,AUC值提升0.021、0.031、0.026,表明耦合模型具有更高评价精度,但精度提升不明显,笔者认为,研究区滑坡灾害数据及评价指标因子精度误差等影响了统计规律的缘故,滑坡易发性分级精度还可进一步研究。

    (5)组合权重模型(CF-AHP-IOE)综合了主、客观权重值,进行滑坡易发性分级,相较于单一模型有更高精度,受结论(4)缘故略低于CF-IOE模型,但在极高易发区灾害密度及频率比值占比均高达90%以上,均高于其他评价模型,准确将灾害区进行划分,因此距离函数-组合权重模型(CF-AHP-IOE)可更好为防灾减灾提供建议。

  • 图  1   大渡河中游地区滑坡分布图和地质条件背景图

    Figure  1.   Map of landslide distribution and geological conditions in the middle reach area of Dadu River

    图  2   大渡河中游地区滑坡危险性初级评价指标体系分级图

    Figure  2.   Grading chart of the primary hazard assessment index system for landslides in the middle reach area of Dadu River Basin

    图  3   变权栅格叠加过程

    Figure  3.   The variational raster overlay process

    图  4   GDIV模型计算流程图

    Figure  4.   The flowchart of GDIV model calculation process

    图  5   滑坡危险性区划图

    Figure  5.   Landslide hazard zoning map

    图  6   滑坡危险性评价结果ROC曲线

    Figure  6.   ROC curve of landslide hazard evaluation results

    表  1   交互作用探测器因子关系

    Table  1   Factor relationships of interaction detectors

    因子关系交互作用
    q(X1X2)<Min(q(X1), q(X2))非线性减弱
    Min(q(X1), q(X2))< q(X1X2)< Max (q(X1), q(X2))单因子非线性减弱
    q(X1X2)> Max (q(X1), q(X2))双因子增强
    q(X1X2)= q(X1)+q(X2)独立
    q(X1X2)> q(X1)+q(X2)非线性增强
    下载: 导出CSV

    表  2   滑坡初级评价指标q值统计

    Table  2   Statistical analysis of primary evaluation index q-values for landslides

    类别指标qp
    地质特征工程地质岩组(X10.1560.000
    与断层距离(X20.0870.000
    地震地震动参数(X30.1640.000
    地形地貌高程(X40.5830.000
    坡度(X50.0210.023
    坡向(X60.0380.003
    地形湿度指数(X70.0170.297
    归一化植被指数(X80.0720.000
    土壤类型(X90.4150.000
    地表水系与河流距离(X100.1580.000
    径流强度指数(X110.0320.015
    降雨年平均降雨量(X120.1820.000
    人类活动与道路距离(X130.1150.000
    下载: 导出CSV

    表  3   部分滑坡初级评价指标交互作用

    Table  3   Interactions of primary evaluation indicators for landslides

    Xi∩Xjq(Xi)q(Xj)q(Xi∩Xj)q(Xi)+q(Xj)交互类型
    X4∩X10.5830.1560.7360.739双因子增强
    X3∩X40.1640.5830.6760.747双因子增强
    X9∩X40.4150.5830.5960.998双因子增强
    X10∩X40.1580.5830.6030.741双因子增强
    X13∩X40.1150.5830.5970.698双因子增强
    X12∩X40.1820.5830.6720.765双因子增强
    X9∩X30.4150.1640.5370.579双因子增强
    X9∩X10.4150.1560.5550.571双因子增强
    X9∩X100.4150.1580.4340.573双因子增强
    X9∩X130.4150.1150.4280.53双因子增强
    X9∩X120.4150.1820.5270.597双因子增强
    X10∩X30.1580.1640.3120.322双因子增强
    X10∩X10.1580.1560.3440.314非线性增强
    X13∩X30.1150.1640.2760.279双因子增强
    X13∩X10.1150.1560.2780.271非线性增强
    X3∩X10.1640.1560.3290.320非线性增强
    X13∩X100.1150.1580.2260.273双因子增强
    X10∩X120.1580.1820.3430.340非线性增强
    X13∩X120.1150.1820.2920.297双因子增强
    X3∩X120.1640.1820.2690.346双因子增强
    X12∩X10.1820.1560.3480.338非线性增强
    下载: 导出CSV

    表  4   危险性评价因子分级与信息量值

    Table  4   Grading and information value of hazard evaluation factors

    评价因子分级信息量值评价因子分级信息量值
    高程/m<2 7002.058年平均
    降雨量/mm
    <750−0.557
    2 700~3 2001.308750~7750.438
    3 200~3 600−1.37775~800−1.014
    3 600~4 000−2.445800~840−0.055
    4 000~4 400−3.76840~880−0.404
    > 4400>880−0.231
    土壤类型淋溶土1.685地震动
    参数
    <0.10.151
    半淋溶土0.1~0.150.464
    初育土−3.9210.15~0.2−1.059
    高山土0.1070.2~0.3
    人为土1.429与道路
    距离/m
    <1001.500
    铁铝土0.890100~2001.227
    与河流
    距离/m
    <400−1.204200~3001.148
    400~800−0.826300~4001.053
    800~1 200−0.025400~5000.789
    1 200~1 6000.004>500−0.335
    1 600~2 0000.577
    >2 0001.038
    工程地质
    岩组
    坚硬岩0.023
    较坚硬岩0.443
    较软岩1.878
    松散土类−1.086
    下载: 导出CSV

    表  5   滑坡危险性评价因子逻辑回归分析结果

    Table  5   Results of logistic regression analysis for landslide hazard evaluation factors

    评价因子BSEWalddfsigExp(B)
    高程4.9920.55182.21010.000147.24
    土壤类型3.0010.55029.78510.00020.110
    工程地质岩组1.6060.8373.38710.0004.666
    年平均降雨量1.1030.3798.46810.0003.013
    与道路距离0.9950.3962.57310.0002.435
    地震动参数0.8020.4691.65710.0001.830
    与河流距离0.1480.3985.25910.0010.739
    常数−7.1320.696104.81510.0000.001
      注:B为模型中各变量的回归系数、SE是标准差、Wald是卡方统计、Sig为显著性水平,dfExp(B)为逻辑回归的结果参数。
    下载: 导出CSV

    表  6   滑坡危险性评价因子权重值

    Table  6   Weight values of landslide hazard assessment factors

    因子q权重
    高程0.5830.329
    土壤类型0.4150.234
    年平均降雨量0.1820.103
    地震动参数0.1640.092
    与河流距离0.1580.089
    工程地质岩组0.1560.088
    与道路距离0.1150.065
    下载: 导出CSV
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