ISSN 1003-8035 CN 11-2852/P

    地质灾害易发性评价因子分级的AIFFC算法优化

    陈宾, 李颖懿, 张联志, 屈添强, 魏娜, 刘宁, 黄春林

    陈宾,李颖懿,张联志,等. 地质灾害易发性评价因子分级的AIFFC算法优化[J]. 中国地质灾害与防治学报,2024,35(1): 72-81. DOI: 10.16031/j.cnki.issn.1003-8035.202210048
    引用本文: 陈宾,李颖懿,张联志,等. 地质灾害易发性评价因子分级的AIFFC算法优化[J]. 中国地质灾害与防治学报,2024,35(1): 72-81. DOI: 10.16031/j.cnki.issn.1003-8035.202210048
    CHEN Bin,LI Yingyi,ZHANG Lianzhi,et al. Classification optimization of geological hazard susceptibility evaluation factors based on AIFFC algorithm[J]. The Chinese Journal of Geological Hazard and Control,2024,35(1): 72-81. DOI: 10.16031/j.cnki.issn.1003-8035.202210048
    Citation: CHEN Bin,LI Yingyi,ZHANG Lianzhi,et al. Classification optimization of geological hazard susceptibility evaluation factors based on AIFFC algorithm[J]. The Chinese Journal of Geological Hazard and Control,2024,35(1): 72-81. DOI: 10.16031/j.cnki.issn.1003-8035.202210048

    地质灾害易发性评价因子分级的AIFFC算法优化

    基金项目: 湖南省创新性省份建设专项(2019RS1059);国家自然科学基金项目(51774131;41972282)
    详细信息
      作者简介:

      陈 宾(1977—),男,河南驻马店人,教授,博士,主要从事地质灾害防治方面的研究工作。E-mail:403021235@qq.com

      通讯作者:

      张联志(1988—),男,江苏沛县人,硕士,工程师,主要研究方向为水工环地质。E-mail:2149859375@qq.com

    • 中图分类号: P694

    Classification optimization of geological hazard susceptibility evaluation factors based on AIFFC algorithm

    • 摘要:

      针对地质灾害易发性评价因子分级数不确定的问题,引入自适应膨胀因子模糊覆盖分级方法(fuzzy cover approach for clustering based on adaptive inflation factor,AIFFC)对易发性评价因子分级进行优化。以湖南省湘乡市为研究区,提取了坡度、坡向、高程、年平均降雨量、归一化植被指数、道路、断层、岩性和土地利用9类评价因子,运用AIFFC及自然断点法(natural breakpoint classification,NBC)对连续型因子进行分级,并分别代入加权信息量模型和随机森林模型,获取研究区易发性区划图。采用单因子分级结果精度、灾积比分析和易发性分区结果对AIFFC分级法的优越性进行检验,结果表明:各因子采用AIFFC算法分级的AUC值均高于自然断点法;基于AIFFC的随机森林模型及加权信息量模型的高易发区灾积比分别提升了56.3%、74.6%,低易发区灾积比分别降低了48%、58.1%,AUC值分别提升了7.6%、2.7%。采用AIFFC分级方法优化了地质灾害易发性评价因子分级,显著提高了地质灾害易发性评价的合理性。

      Abstract:

      This paper addresses the issue of uncertainty in the grading of geological hazard susceptibility evaluation factors and introduces the adaptive expansion factor fuzzy coverage grading method (AIFFC) to optimize the grading of geological hazard susceptibility evaluation factors. Taking Xiangxiang City, Hunan Province as the research area, nine evaluation factors, including slope, slope direction, elevation and average annual rainfall, normalized difference vegetation index for land use, roads, faults, lithology, were extracted. The AIFFC method and the natural breakpoint method were used to grade continuous factors. These graded factors were then incorporated into a weighted information model and random forest model to obtain a susceptibility zoning map for the study area. The superiority of the AIFFC classification method was tested through the comparison of single-factor grading results, disaster product ratio analysis,and ROC curve comparison of susceptibility zoning results. Based on AIFFC, the hazard accumulation ratio of the random forest model and the weighted information entropy model in the high susceptibility areas increased by 56.3% and 74.6%, respectively, while in the low susceptibility areas, it decreased by 48% and 58.1%, respectively. The AUC values increased by 7.6% and 2.7%, respectively. The AIFFC classification method is used to optimize the evaluation factor classification of geological disaster susceptibility, which significantly improves the rationality of the evaluation of geological disaster susceptibility.

    • 我国是世界上最大的黄土分布区,总面积约6.3×105 km2,占国土总面积的6.6 %[1]。由于黄土的类卡斯特结构,导致黄土具有强烈的水敏性,天然状态下的黄土具有极高的强度,遇水后,强度急剧降低,易发生软化变形,进而形成黄土地质灾害。据统计,90 %以上的黄土地质灾害发生与水有关[2-3]。甘肃黑方台是我国最著名的水致黄土滑坡研究场地之一,被称为黄土滑坡研究的天然试验场。从1968年至今,几十年农业漫溉使黑方台地下水位大幅抬升,塬边黄土滑坡频繁发生[4],引起了国内外学者广泛关注,由此开展了大量的研究。例如:在滑坡类型及分布规律方面,任晓虎[5]基于多种滑坡识别指标对黑方台黄土滑坡发育早期特征识别进行研究。许领等[6]等利用IKONOS影像与DEM叠加重现了滑坡场景,对黑方台滑坡进行了系统的分类;在成因机理方面,张茂省等[7-8]、王家鼎等[9]、潘攀[10]、蔺晓燕[11]对灌溉水诱发黄土滑坡的形成机理进行了较系统研究;在风险评估方面,彭大雷[12]在研究归纳了各类型灾害的形成条件、致灾因子、成灾模式等的基础上,提出黄土滑坡潜在隐患地质识别方法。薛强等[13]基于多期DEM数据,利用GIS技术,通过滑坡体积与速度乘积计算滑坡强度,对黑方台南塬典型滑坡进行了危险性定量评估。然而随着滑坡的频繁发生,当地居民安全问题异常突出,而黑方台黄土滑坡的运动特征方面的研究直接关乎着滑体最终的致灾范围和灾害效应,影响当地居民生命财产。

      针对黄土滑坡的运动特征研究,数值模拟是最常用也是最可视化的手段。例如:王思源[14]运用FLAC3D有限差分模拟软件,分析了黑方台焦家滑坡的实际变形破坏过程;胡炜等[15]在实际测试及工程地质测试的基础上,基于有限差分法的Sassa运动学模型模拟焦家崖头滑坡运动全过程,并分析滑坡的速度变化规律;贾俊等[16]以离散元模型对焦家崖头13号滑坡为例,研究地下水对滑坡作用机制及灌溉型黄土滑坡破坏过程和高速远程运动学特征; 赵纪飞等[17]以黑方台焦家滑坡为例,基于野外调查和室内试验,通过Sassa运动学模型模拟该滑坡泥流运动过程。考虑到黑方台地区的黄土滑坡具有很强的连续性和流动性,而Massflow基于深度积分的连续介质力学方法,将运动物体视作连续流体,可用来模拟碎屑流、滑坡、泥石流、山洪等地质灾害动力演化全过程,且建模效率高、计算速率快,故采用该软件反演黑方台黄土滑坡较为适用,已在多个案例[1218-21]得到应用。

      文中以黑方台2019年10月5日发生在党川村附近的一起黄土滑坡为研究对象,通过野外科学考察,分析该滑坡的基本特征,并采用Massflow软件反演该滑坡运动全过程,为黑方台地区滑坡风险评估及防灾减灾提供一定的科学参考。

      黑方台位于甘肃省永靖县盐锅峡镇境内,地处黄河与湟水河交汇处上游,距兰州市约40 km(图1),属于黄河Ⅳ级阶地,台塬总面积约14 km2,塬面东西向长约7.7 km,南北向宽约2.5 km。台塬被虎狼沟切割为黑台和方台,东侧为黑台,面积约9 km2,西侧为方台,面积约2 km2。台塬出露地层自上而下为:全新统崩滑堆积物(Q42del);上更新统马兰黄土(Q3eol);中更新统粉质黏土(Q2al);中更新统砂卵石层(Q2al);下白垩统河口群粉砂质泥岩(K1hk)(表1)。

      图  1  研究区地理位置图
      Figure  1.  Geographical location of the study area
      表  1  研究区地层岩性
      Table  1.  Stratigraphic lithology of the study area
      代号岩性
      第四系全新统Q42del堆积物,由滑后的黄土、卵砾石及砂泥岩
      第四系上更新统Q3eol灰黄色黄土,以粉粒为主,厚30~50 m
      第四系中更新统Q2al褐红色粉质黏土层,厚3~10 m
      第四系中更新统Q2al粉砂细层、砂卵石层,厚1~10 m
      白垩系下统K1hk紫红色-暗红色泥岩、砂质泥岩,厚度>70 m
      下载: 导出CSV 
      | 显示表格

      研究区内黄土滑坡具有群体性分布特征[6]图2),根据黑方台地区内物质组成和运动特征,将黑方台滑坡分为四大类:(1)黄土—错落型;(2)黄土—碎屑流型;(3)黄土—泥流型;(4)黄土基岩—滑移型。

      图  2  黑方台滑坡分布图
      Figure  2.  Distribution of landslides in the Heifangtai platform

      “10·5”滑坡发生于2019年10月5日,位于甘肃黑方台党川村,据当地居民介绍,该滑坡运动时间极短,不超过1 min。该滑坡为黄土—碎屑流型滑坡,基本没有水的影响,多为崩塌型的黄土内部滑动,发生于塬边裂缝发育和内部存在软弱结构面的Q3黄土中,通常情况下前期次滑坡增加坡体的临空条件,并在坡体后缘形成一定数量的拉张裂缝,为黄土—碎屑流型滑坡的发生创造条件,坡体失稳破坏之后,在运动过程中逐渐破碎解体。“10·5”滑坡存在多期性、隐蔽性、突发性及影响范围广等特点(图3)。

      图  3  滑坡基本特征
      Figure  3.  The characteristics of the landslide

      滑坡区在地貌单元上属于黄土高原丘陵沟壑区,整体位于黄河Ⅳ级基座阶地,滑坡所处党川段为目前以来滑坡发育最活跃的区域。滑坡区斜坡坡体上陡下缓,滑坡后壁坡度范围为60°~85°,滑坡前缘为较平缓开阔农田,坡度范围20°~25°,平均坡度约50°,这种后缘高陡、前缘开阔的地形特点,为滑坡的发生提供了良好的临空条件。

      “10·5”滑坡的滑体物质成分主要由风成黄土组成,位于滑坡上部,厚度约35 m,岩性为马兰黄土,浅黄色,土体松散,遇水后土体体积含水量迅速增大,导致黄土黏聚力大幅降低,发生软化,坡体稳定性降低,滑动面在黄土层非饱和带,剪出口在斜坡中上部,多在坡脚形成堆积。坡体及周边出露地层主要有马兰黄土、粉质黏土、砂卵石层、粉砂质泥岩[22-24]图4)。

      图  4  “10·5”滑坡剖面图
      Figure  4.  The profile of the “10·5” landslide

      根据野外实地调查,该滑坡整体平面形态呈长椅型,后壁近似圆弧状,后壁宽约110 m,顺坡长约355 m,后缘高程1710 m,前缘高程1605 m,相对高差105 m,坡体后缘后退约11 m,滑源区滑体平均厚度30 m,滑体在坡脚形成堆积区,呈扇形展布,堆积区地形较为平缓,滑体极为松散,滑坡前缘堆积区宽度不一,范围在70~140 m(图3),滑坡区域面积为700 m2,体积约2.7×104 m3,属于小型黄土滑坡。

      坡体后缘发育数条拉张裂缝,平均宽度5~20 cm,走向平行于坡边,竖向上呈上宽下窄的特点,深度受坡高、坡度等因素控制,分布形态为直线型,呈现出渐近式后退的趋势,有明显错台,其错台最大下错量可达90 cm,平均下错大于20 cm。选取一条具有明显变化的裂缝1,通过几期不同时间段的对比,裂缝横向长度与宽度逐渐增大,在201912期前,斜坡土体沿裂缝1贯通发生崩塌滑移,并在坡顶后部发育多条拉张裂缝(图5)。

      图  5  滑坡后缘错台及局部裂缝变化过程
      Figure  5.  Dislocation of the trailing edge of the landslide and the change of local fractures

      据野外考察发现,滑源区土体高度临空,后缘受拉张应力影响,裂缝不断扩张,最终形成滑动面发生剪切破坏(图6),滑源区土体在重力作用下失稳破坏以235°方向开始滑动,坡下地形无约束,滑坡在运动过程中未发生较大的改道行为,滑坡东临山脊,部分滑体受到右侧山脊的碰撞,滑至底部平缓地区时,向西南方向发展。滑坡剪出口位置较高,滑体储有较大势能,滑坡前缘到达斜坡底部之后继续向前运动,穿过底部农田和灌溉渠道直至停止,滑坡前缘最远处距公路60 m(图7)。

      图  6  滑坡发育过程
      Figure  6.  The developping process of landslide
      图  7  滑坡运动过程路径
      Figure  7.  Motion path of the landslide

      采用Massflow软件进行滑坡特征反演,该软件是基于深度积分的连续介质力学方程而建立的有限差分法数值软件,能够很好地考虑运动过程中地形地貌对其运动的影响。该软件把复杂地质模型简化为上下界面,计算公式如下[18,21]

      h1ρ1v1t+h1ρ1u1v1x+h1ρ1u1v1y=ρ1gh1(h1+z10)yτ1+ρ1v1(z1)E1 (1)

      式中:ρ1——流动层密度/(kg·m−3);

      τ1——上部流动层对底部的剪力/kPa;

      z10——顶部流动层的基底边界/m;

      E1——底部稳定层的夹带率;

      u1、v1——材料边界速率/(m·s−1);

      h1——流体厚度/m;

      g——重力加速度,取9.8 m/s2

      该软件内有Coulomb、Manning、Voellmy三种计算模型,其中Coulomb模型适用于滑坡、岩崩等碎屑流(固相)、Manning模型适用于溃坝、洪水等水力学灾害(液相)、Voellmy模型适用于泥浆,泥石流等灾害(固液相)。文中采取Coulomb计算模型,进行初步模拟计算,Coulomb模型其主要计算方程如式(2),关于软件的基本力学方程,可参考欧阳朝军等的相关研究[18]

      τb=c+ρ¯gh(1λ)tanφ (2)

      式中:τb——基底摩檫力/kPa;

      cφ——土体黏聚力和内摩擦角/(°);

      ρ¯——材料平均密度/(kg·m−3);

      g——重力加速度,取9.8 m/s2

      h——滑坡竖向厚度/m;

      λ——基底液化系数,取值为0~1。

      关于滑坡前后地形数据及影像的获取,文中通过无人机航测获得,将所获DEM数据导入ArcGIS10.7中进行处理,得到滑坡物源区范围,将滑坡地形和物源区栅格数据转换为Massflow能够识别的ASCII格式。

      模拟所需参数有土体密度(ρ)、黏聚力(c)、基底液化系数(λ)、岩土体内摩擦角(φ),参考黑方台地区已有室内实验数据及参数[12,25],将模拟所需非关键参数土体密度与内摩擦角视为定值。对于黏聚力与基底液化参数,这两者主要影响着滑坡的堆积形态和堆积厚度,滑坡的基底液化系数和黏聚力通过控制变量法确定,主要参数取值见表2

      表  2  模拟计算参数
      Table  2.  Simulation parameters
      参数ρ/(kg·m−3c/kPaφ/(°)λg/(m·s−2
      取值14005.532°0.769.8
      下载: 导出CSV 
      | 显示表格

      通过反演计算得到黏聚力和基底液化系数最佳取值,滑体物质主要是黄土,滑床基本为老的滑坡堆积体,因此滑坡黏聚力可简化为一个定值,黄土浸水后其黏聚力大幅下降[26-27],故黏聚力取值范围设为0~7 kPa。第一轮反演将基底液化系数视为定值(取0.4),黏聚力在0.5 kPa的梯度取值条件进行反演试算,模拟结果与滑坡实际范围进行对比,确定黏聚力值;第二轮反演在黏聚力为5.5 kPa时,基底液化系数λ以0.1的梯度进行反演试算,发现在0.6~0.8区间结果较好,进而将参数以0.01的梯度细化,得到更精确的基底液化系数λ

      根据表2中模拟计算参数进行计算,模拟滑坡不同时间段的运动特征及滑坡体的堆积范围和厚度,直至运动停止。该滑坡为多期次滑坡,所获滑坡前后地形数据时间分别为2019年5月及2019年12月,通过前后DEM数据所获滑源区土体大于“10·5”滑坡实际滑源区,并且期间滑坡上部土体存在多次崩落的可能性,崩落土体因重力作用落于坡面上,改变了滑坡下垫面微地形,使得堆积区右侧地形高于左侧,滑体向下运动过程中向两侧扩展,因滑坡右侧为一山脊,故在滑坡模拟过程中,滑源区土体在重力作用下向下运动时,东侧滑体受到右侧山脊的碰撞,发生一定程度的破碎、折返运动,随即向西南方向发展运动堆积,模拟所得结果与实际堆积范围出现些许偏差。

      通过对滑坡各时间段运动过程的模拟,得到了不同时间段的堆积范围(图8),滑坡体初始厚度最大约16 m,滑坡启动后,快速冲向坡脚和斜坡两侧,并最终堆积在滑坡前缘耕地处。图8中(b)可看出t=5 s时滑坡已启动,滑源逐渐向坡脚滑动,此时滑坡最大堆积厚度为7.08 m,t=10 s时滑坡主体部分已基本抵达坡脚,最大堆积厚度为3.36 m,t=15 s时,滑坡在滑坡前缘耕地处堆积,t>15 s时滑坡体在前缘耕地处缓慢流动至停止,45 s左右时滑坡已基本停止运动。

      图  8  滑坡不同时刻堆积范围
      Figure  8.  The simulation results of landslide accumulation boundary at different times

      滑坡运动是一个动态运动过程,为定量分析滑坡堆积厚度分布特征,选取t=45 s的模拟结果,在滑坡的主滑方向、滑源区与滑坡前缘三个位置提取堆积厚度,与真实堆积情况对比。根据滑坡前后高程数据对比,获得滑坡真实堆积厚度分布(图9)。对于主滑方向的1-1′剖面,滑移距离在约355 m处基本达到收敛,滑坡运动方向的反演堆积厚度与实际堆积特征相符,堆积区堆积厚度整体趋势为先增大减小,平均堆积厚度逐渐变小,最大堆积厚度约为0.82 m。对于3-3′剖面,因滑坡前缘地形无约束且滑体流动性较强,在滑坡物源体积一定的情况下,滑体向前运动的同时向两侧扩散堆积,主要以散状堆积在坡脚,呈扇形展布,实际平均堆积厚度约0.63 m,模拟平均堆积厚约0.49 m。由2-2′剖面看出,在滑坡滑源区附近,坡体崩塌下滑,故滑源区堆积厚度为负值,2-2′剖面真实下滑最大深度约14.86 m,反演结果与真实下滑深度一致。

      图  9  滑坡剖面位置及堆积厚度图(t=45 s)
      Figure  9.  Location of typical profile and accumulate thickness (t=45 s)

      通过三个剖面堆积厚度的比较,可发现在主滑方向与滑源方向上的堆积厚度变化和实际情况一致,滑坡前缘模拟堆积厚度与实际情况存在误差,这是因为滑坡堆积区的整体厚度均小于1 m,在小范围厚度上误差体现比较明显,同时也因滑坡运动路径受地形和前期堆积体的限制,运动过程中发生了碰撞折返,故在模拟滑坡运动时,滑体到达实际堆积区范围后,继续向西南方向运动堆积,模拟所得堆积区范围略大于实际堆积区范围,使得滑坡前缘处模拟堆积厚度小于实际堆积厚度。

      图10为该滑坡不同时刻运动速度模拟结果。为获取滑坡不同部位在不同时刻的运动速度,在滑坡主滑方向上布置速度监测点1−5号,得出1−5号监测点滑坡运动速度变化图。

      图  10  不同时刻滑坡运动速度
      Figure  10.  The movement speed of the landslide at different times

      滑坡自启动到停止运动总用时45 s,可将其运动堆积过程分为启动加速-稳定加速-减速堆积三个阶段(图11):0~7.2 s内为启动加速阶段;7.2~11.3 s为稳定加速阶段;11.3~45 s为减速堆积阶段,滑坡动能逐渐耗尽,最终停止。其中启动加速阶段与稳定加速阶段时间较短,仅占运动总时间的25.1 %,说明“10·5”滑坡具有突发性,坡体上部迅速崩塌滑动,在极短的时间内速度达到最大值,减速堆积阶段占据滑坡运动主要部分,约74.9 %,说明滑坡具有较强的流动性。

      图  11  运动速度-时间变化曲线
      Figure  11.  Relationship between the movement speed and time

      从5个速度监测点获得的数据来看,到达每个监测点的最大速度数值相差较小,滑坡各点运动速度均表现为先快速增大后迅速减小。坡下横向地形无约束,能量消散快,故滑坡运动速度变化迅速。对于1号监测点,坡体上部土体开始崩塌失稳,滑动开始约1 s运动速度达到最大值16 m/s,因坡体上部存在些许土体掉落,在减速阶段速度未能减至0;2号监测点上,滑坡滑源物质经4 s左右达到最大速度23 m/s,17 s时速度趋于0;3号监测点上,滑坡历经7 s达到最大速度26 m/s,25 s时速度趋于0;4号监测点上,滑坡历经11 s达到最大速度24.6 m/s,30 s时速度趋于0;5号监测点位于堆积区前缘,滑坡动能已耗散部分,故此处最大速度较低为18.5 m/s,在45 s时趋于0。结合运动速度-时间变化曲线图可以看出,滑坡从后缘至前缘处逐步达到静止状态,且在滑坡不同部位,滑坡运动速度从加速直至降低为0的时间间隔都比较小,进一步说明了“10·5”滑坡运动速度快。

      文中通过野外考察研究,对黑方台党川村“10·5”滑坡的基本特征及运动特征进行了一定的讨论,并利用Massflow数值模拟软件反演滑坡运动全过程。得到以下结论:

      (1)“10·5”滑坡体积约为2.7×104 m3,主滑方向为235°,最大滑距355 m,具有突发性,影响范围广的特点。

      (2)根据滑坡运动速度可将“10·5”滑坡运动堆积过程分为启动加速-稳定加速-减速堆积三个阶段,自启动到停止整个过程仅持续45 s,其中滑坡最大运动速度可达26 m/s,堆积区最大堆积厚度为0.82 m。

      (3)通过1—5号速度监测点的数据可看出,“10·5”滑坡运动速度呈先快速增大后迅速减小的趋势,所用时间极短。

      (4)经过Massflow数值模拟获得的滑坡危险范围较大于真实范围,存在些许偏差,结果偏于保守。

    • 图  1   湘乡市地质灾害分布图

      Figure  1.   Geological disaster distribution map of Xiangxiang City

      图  2   易发性各评价因子图层

      Figure  2.   Layers of susceptibility evaluation factors

      图  3   各模型易发性分区图

      Figure  3.   Susceptibility zoning maps of each model

      图  4   单因子ROC曲线分析图

      Figure  4.   Single-factor ROC curve analysis

      图  5   各模型ROC曲线图

      Figure  5.   ROC curve for various models

      表  1   各评价因子AIFFC分级参数

      Table  1   AIFFC classification parameters for each evaluation factor

      评价因子 研究区范围 灾害点分布范围 di λ 分级数 分级区间({ρl})
      坡度/(°) [0,71.26] [2,70] 1.0~2.0 8 7 {1, 12, 125, 35, 14, 49, 34}
      坡向/(°) [0,360] [0,360] 1.0~1.67 20 12 {10, 11, 19, 23, 38, 52, 25, 30, 25, 15, 9, 13}
      高程/m [31.90,800.44] [113.12,456.70] 1.0~2.0 40.33 8 {112, 92, 34, 22, 5, 3, 1, 1}
      年平均降雨量/mm [1202.78,1600.43] [1221.03,1554.89] 1.0~1.9 22.81 10 {1, 2, 14, 19, 59, 70, 60, 36, 8, 1}
      归一化植被指数 [0,0.61] [0.015,0.417] 1.0~1.5 0.04 6 {7, 27, 48, 89, 85, 14}
      距道路距离/m [0,>3483.20] [326.11,3483.20] 1.0~1.79 230.84 10 {102, 55, 33, 37, 16, 10, 10, 5, 1, 1}
      距断层距离/m [0,>4634.35] [423.71,4634.35] 1.0~1.5 236.17 11 {75, 57, 43, 38, 10, 16, 10, 6, 5, 5, 5}
      下载: 导出CSV

      表  2   AIFFC分级结果

      Table  2   AIFFC classification results

      评价因子 分级数 分级结果
      坡度/(°) 8 >0~2;>2~22;>22~35;>35~48;>48~55;>55~65;>65~70;>70
      坡向/(°) 12 >0~30;>30~68;>68~90;>90~125;>125~162;>162~190;>190~225;>225~260;>260~280;
      >280~310;>310~338;>338~360
      高程/m 9 >31.9~113.12;>113.12~165.57;>165.57~221.23;>221.23~278.54;>278.54~333.59;>333.59~367.50;
      >367.50~408.51;>408.51~456.70;>456.70
      年平均降雨量/mm 11 >1 202.70~1 221.03;>1 221.03~1 283.85;>1 283.85~1 336.92;>1 336.92~1 368.54;>1 368.54~1 410.03;
      >1 410.03~1 446.28;>1 446.28~1 484.91;>1 484.91~1 516.62;>1 516.62~1 536.87;>1 536.87~1 554.89;
      >1 554.89
      归一化植被指数 7 >0~0.015;>0.015~0.180;>0.180~0.246;>0.246~0.316;>0.316~0.377;>0.377~0.417;>0.417
      距道路距离/m 11 0~326.11;>326.11~664.23;>664.23~989.03;>989.03~1368.63;>1 368.63~1 688.75;>1 688.75~2 075.72;
      >2 075.72~2 497.47;>2 497.47~2 831.94;>2 831.94~3 065.30;>3 065.30~3 483.20;>3 483.20
      距断层距离/m 12 0~423.71;>423.71~850.88;>850.88~1 272.26;1 272.27~1 694.48;1 694.49~2 150.42;2 150.43~2 485.00;
      2 485.01~3 005.74;3 005.75~3 523.15;3 523.16~4 107.21;4 107.22~4 248.58;4 248.59~4 634.35;>4 634.35
      下载: 导出CSV

      表  3   NBC分级结果

      Table  3   Natural break point method grading results

      评价因子 分级数 分级结果
      坡度/(°) 8 0~3.91;>3.91~9.50;>9.50~15.37;>15.37~20.95;>20.95~26.26;>26.26~31.85;>31.85~38.56;>38.56~71.26
      坡向/(°) 12 0~24.48;>24.48~ 57.04;>57.04~88.18;>88.18~ 117.91;>117.91~147.64;>147.64~ 177.37;>177.37~ 208.52;>208.52~239.66;>239.66~269.39;>269.39~ 299.12;>299.12~ 328.85;>328.85~ 360.00
      高程/m 9 31.90~79.93;>79.93~121.96;>121.96~166.99;>166.99~218.03;>218.03~275.07;>275.07~341.11;>341.11~425.17;>425.17~542.26;>542.26~800.44
      年平均降雨量/mm 11 1 202.70~1 289.62;>1 289.62~1 333.91;>1 333.91~1 363.46;>1 363.46~1 386.40;>1 386.40~1 405.55;>1 405.55~
      1 425.18;>1 425.18~1 444.75;>1 444.75~1 463.71;>1 463.71~1 488.88;>1 488.88~1 523.98;>1 523.98~1 600.42
      归一化植被指数 7 0~0.051;>0.051~0.152;>0.152~0.208;>0.208~0.259;>0.259~0.303;>0.303~0.347;>0.347~0.611
      距道路距离/m 11 0~71.14;>71.14~167.93;>167.93~287.96;>287.96~398.67;>398.67~534.77;>534.77~757.79;>757.79~1 088.95;
      >1 088.95~1 532.08;>1 532.08~2 160.22;>2 160.22~3 483.20;>3 483.20
      距断层距离/m 12 0~93.41;>93.41~195.93;>195.93~317.53;>317.53~464.40;>464.40~667.68;>667.68~850.88;>850.88~1 059.58;
      >1 059.58~1 363.72;>1 363.72~1 914.46;>1 914.46~2 795.16;>2 795.16~4 634.35;>4 634.35
      下载: 导出CSV

      表  4   易发性评价结果灾积比统计表

      Table  4   Statistical table of disaster accumulation ratio - product ratio for susceptibility evaluation results

      评价
      模型
      易发性
      分区
      面积
      占比/%
      灾积比 评价
      模型
      易发性
      分区
      面积
      占比/%
      灾积比
      NBC-MIV 高易发 13.62 0.611 AIFFC-MIV 高易发 11.73 0.955
      中易发 25.42 0.178 中易发 19.61 0.147
      低易发 60.96 0.050 低易发 68.76 0.026
      NBC-RF 高易发 15.03 0.755 AIFFC-RF 高易发 9.68 1.318
      中易发 28.53 0.096 中易发 25.86 0.083
      低易发 56.44 0.031 低易发 64.45 0.013
      下载: 导出CSV
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    • 收稿日期:  2022-10-27
    • 修回日期:  2022-11-12
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