Application of different machine learning models in landslide susceptibility assessment in Badong County, Hubei province
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摘要:
中国是世界上发生滑坡灾害最频繁的国家之一，滑坡易发性评价有助于防灾减灾工作。由于不同机器学习模型在不同区域的适配程度不同，为更好开展湖北省巴东县的滑坡灾害防治工作，选取坡度、坡向、曲率、起伏度、地层、覆盖层、NDVI、道路密度、水系密度、斜坡结构10个影响因子，采用逻辑回归（LR）、支持向量机（SVM）、多层感知机（MLP）和随机森林（RF）四种模型进行滑坡易发性评价。并通过受试者工作特征曲线（ROC）、均方误差与决定系数等指标、滑坡-研究区占比三种评价方式用于评价模型精度。实验结果表明，不同模型在不同评价方式中存在差异，但总体而言，RF模型精度最高且绘制出的易发性分区图更合理。四个模型绘制的易发性区域分布图相似，极高易发区和高易发区主要分布于南边沿江地区，西南沿岸的官渡口镇、焦家湾村等附近地区表现出较高易发性，该评价结果可以为巴东县的滑坡治理提供参考。
Abstract:China is one of the countries most frequently affected by landslide disasters in the world, making landslide susceptibility assessment crucial for effective disaster prevention and mitigation. Due to variations in the adaptability of different machine learning models in different regions, in order to better carry out landslide disaster prevention and control work in Badong County, Hubei Province, ten influencing factors including slope gradient, slope direction, curvature, degree of undulation, stratigraphy, overburden, NDVI, road density, water system density, and slope structure were selected. Four different models, including Logistic Regression (LR), Support Vector Machine (SVM), Multilayer Perceptron (MLP), and Random Forest (RF), were used for landslide susceptibility evaluation. Three evaluation methods were used to assess the accuracy of the model: Receiver Operating Characteristic (ROC) curves, mean square error, determination coefficient, and the ratio of landslide to study area. The experimental results show that there are differences among the models in different evaluation methods. Overall, the RF model exhibits the highest accuracy and generates more reasonable susceptibility zoning maps. The susceptibility distribution maps generated by the four models are similar, with high and very high susceptibility areas predominantly located in the southern riverside area. Areas near Guandukou Town and Jiaojiawan Village along the southwest coast exhibit relatively high susceptibility. The assessment results can provide reference for landslide control in Badong County.
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0. 引 言
近年来，中国建设开发了数十座软岩露天煤矿，在开采过程中采场及排土场均发生过一定规模的滑坡，对于采场底帮顺倾软岩边坡与顺倾软基底内排土场边坡滑坡灾害尤为严重。滑坡灾害直接影响剥采排工程的发展，造成人员伤害和设备损毁及地貌景观破坏，严重制约着露天矿的安全高效生产[1-2]，边坡稳定性治理问题已成为边坡工程领域亟待解决的难题之一。
目前国内外学者们应用不同理论对其展开大量有意义的研究，成果丰硕。王东等[3]综合运用极限平衡法及数值模拟法，分析了不同压帮高度下边坡稳定性变化规律，提出了逆倾软岩边坡变形的治理措施；刘子春等[4]以扎尼河露天矿为背景，通过分析扩帮、内排压角等治理措施的基础上，提出了一种条带式开采技术的边坡治理方案；陈毓等[5]采用ANSYS对黑山露天矿内排土场边坡稳定性和破坏机理进行了分析，揭示了内排土场滑坡模式为“坐落滑移式”滑动，运用削坡治理技术来保证内排土场稳定性；唐文亮等[6]系统分析了露天矿内排土场滑坡影响因素，提出了预留煤柱的滑坡治理方法；李伟[7]揭示了阴湾排土场边坡变形破坏机理并结合数值模拟法和极限平衡法，分析了内排不同压脚方案下边坡稳定性，提出了阴湾排土场滑坡治理措施；王刚等[8]基于有限元数值模拟法和极限平衡法，分析了边坡破坏机理并对边坡进行了稳定性计算，提出了削坡减载的治理措施。软岩露天煤矿采场边坡稳定性治理最经济有效的方式是内排追踪压帮，内排土场稳定是前提，但现有方法均是单一针对采场或排土场边坡稳定性分析和治理，未能同时兼顾采场与内排土场边坡的稳定性，对工程实际的指导性不强。
本文以贺斯格乌拉南露天煤矿首采区南帮为工程背景，在兼顾采场与内排土场边坡稳定性的基础上，提出了露天煤矿顺倾软岩边坡内排追踪压帮治理工程，为深入研究顺倾软岩露天煤矿边坡稳定性治理方法提供新的参考。
1. 边坡工程地质条件分析
贺斯格乌拉南露天煤矿设计生产能力为15 Mt/a，首采区南帮地层自上而下主要发育第四系、2煤组、2煤组与3煤组间夹石、3煤组、3煤组底板和盆地基底火山岩，含煤岩系主要以泥岩为主，全区可采的有2-1、3-1煤层，第四系以粉砂质黏土为主，局部夹黄-浅灰色细砂及含砾粗砂层，岩性较差，首采区土层赋存较薄，且其地层中多赋存软弱夹层，主要以3-1、3-4煤底板弱层主，属于典型的顺倾软岩边坡，岩土体物理力学指标如表1所示，典型工程地质剖面如图1所示。
表 1 岩土体物理力学指标Table 1. Physical and mechanical parameters of rock mass岩体名称 内摩擦角/（°） 黏聚力/kPa 容重/（kN·m−3） 弹性模量/MPa 泊松比 砂岩 26.00 65 19.6 35 0.42 粉质黏土 14.06 22 19.8 46 0.38 煤 29.00 85 12.1 40 0.35 泥岩 20.00 40 19.4 75 0.36 排弃物 14.49 20 19.0 60 0.40 弱层 6.00 0 19.1 20 0.42 回填岩石 20.00 40 19.0 − − 2. 采场底帮浅层边坡二维稳定性分析
影响顺倾软岩露天煤矿采场边坡稳定性的主控因素是弱层及其暴露长度，采用追踪压帮方式治理该类边坡稳定性时，可忽略软弱夹层为底界面的切层-顺层组合滑动模式[9-10]，仅考虑剪胀破坏模式。由于贺斯格乌拉南露天矿边坡体内赋存软弱夹层，主要以3-1、3-4煤底板弱层为主，顺倾角度大，岩质松软，对于此类边坡，浅部可通过平盘参数进行重新设计，深部必须利用三维效应，实现稳定性控制。可采用刚体极限平衡法中的剩余推力法对浅层边坡进行稳定性计算[11-12]。该方法的优点是可以用来计算求解给定任意边坡潜在滑面的稳定系数，并且可以考虑在复杂外力作用下的不同抗剪参数滑动岩体对边坡稳定性的影响。稳定系数求解公式为：
$$ {P_i} = \frac{{{W_i}\sin {\alpha _i}({W_i}\sin {\alpha _i}\tan {\varphi _i}) + {C_i}{L_i}}}{{{F_{\rm{s}}}}} + {\phi _i}{p_{i - 1}} $$ (1) $$ {\phi _i} = \frac{{\cos ({\alpha _{i - 1}} - {\alpha _i})\tan {\varphi _i}\sin ({\alpha _{i - 1}} - {\alpha _i})}}{{{F_{\rm{s}}}}} $$ (2) 式中：
${P_i}$ ——第$i$ 条块的剩余推力/kN；$ {W_i} $ ——第$i$ 条块的重量/（N·m−3）；$\alpha_i$ ——第$i$ 条块的滑面倾角/（°）；${\varphi _i}$ ——第$i$ 条块的推力传递系数；${C_i}$ ——第$i$ 条块的滑面黏聚力/kPa；${L_i}$ ——第$i$ 条块的底面长度/m；${\phi _i}$ ——第$i$ 条块的滑面摩擦角/（°）；${F_{\rm{s}}}$ ——稳定性系数。依据《煤炭工业露天矿设计规范》（GB 50197―2015）[13]综合考虑贺斯格乌拉南露天煤矿首采区南帮边坡服务年限、地质条件与力学参数的可靠性、潜在滑坡危害程度等，确定安全储备系数为1.2。
由于南帮压覆大量煤层，在保证安全前提下，为实现最大限度回采压覆的煤炭资源，需要对边坡形态重新设计。本文选取典型剖面为研究对象，浅层边坡形态按照40 m运输平盘、15 m保安平盘进行设计，深部利用横采内排三维支挡效应回采采场底帮深部压覆煤炭资源。通过上述情况对浅层边坡进行了分析，边坡稳定性计算结果如图2所示。
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图 2 浅层边二维坡稳定性计算结果Figure 2. Calculation results of two-dimensional slope stability of the shallow side分析图2可知，浅部边坡形态可按照40 m运输平盘、15 m保安平盘进行设计，由于弱层上部存在煤岩支挡，边坡潜在滑坡模式为以圆弧为侧界面、3-1煤底板弱层为底界面、沿边坡坡脚处剪出，此时，浅层边坡能满足安全储备系数1.2的要求。
3. 采场底帮深部边坡稳定性三维效应分析
基于浅层边坡二维稳定性分析结果可知，实现深部稳定性控制，必须借助横采工作帮与内排土场的双重支挡作用进行压煤回采，因此提出了利用横采内排三维支挡效应回采采场深部压覆煤炭资源[14]。本文借助FLAC3D数值模拟软件，分析不同降深角度和不同追踪距离条件下的边坡三维稳定性，以期获得最优的边坡空间形态参数。
（1） 模型的建立
考虑到FLAC3D建模较为复杂，采用CAD与Rhino相结合的方法，首先在CAD中对剖面进行整理，然后在Rhino软件中进行模型成体与网格划分的处理，并用Griddle将网格导出，生成精细的六面体网格模型[15 − 17]，最后导入采用于FLAC3D进行数值模拟计算。为尽可能凸显边坡稳定性的三维效应，以南帮断面形态设计边坡为数值模拟对象，共计建立15种工况模型，模型如图3，追踪距离分别为50，100，200，300，400 m。为避免边界效应，在模型的底部和两侧分别施加水平和垂直位移约束，加载方式为重力加载[18]。
（2） 计算结果分析
由于计算结果过多，本文仅列举降深角度α=29°，追踪距离50，200，400 m工况下边坡位移云图（切割位置为沿模型走向中间处），如图4所示。南帮边坡三维稳定性计算结果如图5所示。
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图 5 追踪距离与边坡稳定系数的关系曲线Figure 5. Relationship curve between tracking distance and slope stability coefficient分析图4、图5可知，追踪距离50 m时，三维支挡效应显著，边坡深部位移明显小于上部，发生以圆弧为侧界面、3-1煤底板弱层为底界面的切层-顺层-剪出滑动，稳定系数大于1.2。当追踪距离大于50 m时，通过对比分析不同深部边坡角（α）条件下的数值模拟结果可知，深部边坡角对边坡稳定性系数影响较小，随着追踪距离的增加，边坡的破坏模式过渡为以圆弧为侧界面、3-1煤底板弱层为底界面的切层-顺层滑动，并且此时边坡的稳定性不满足安全储备系数1.2要求。因此，内排土场追踪距离需控制在50 m以内，深部边坡角设计为29°。
4. 内排土场压帮边坡稳定性分析与治理
露天矿内排土场边坡稳定的主控因素是软弱基底，软弱基底分为自身软弱岩土层和受外界条影响转变为软弱岩土层2种类型。排土场下沉是软弱基底内排土场失稳的特征，主要现象是含有纵向强烈挤压区，基底上部岩层隆起，地面出现滑坡等[19 − 21]。在保证采场南帮安全的前提下降深至3-1煤底板，须借助横采工作帮与内排土场的双重支挡作用，内排土场稳定是前提[22]。由于内排土场基底为3-1、3-4煤底板弱层，顺倾角度较大，按照内排土场设计参数，其稳定性无法满足安全储备系数的要求[23]。从提供基底强度角度出发，采用破坏弱层回填岩石的方式提高内排土场边坡稳定性。按照排土台阶高度24 m、平盘宽度60 m、坡面角33°对不同内排压帮标高边坡稳定性进行试算，确定内排最小压帮标高为+844水平，因此本文分析了内排基于+844水平的压帮高度下内排土场基底不同的处理方式时的边坡稳定性计算结果如图6—7所示，边坡稳定性与破坏弱层回填岩石范围关系曲线如图8所示。
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图 6 3-1煤层内排基底不同处理方式下边坡稳定性计算结果Figure 6. Calculation results of slope stability under different treatment methods of inner row basement (3-1)分析图6—图8可知，当内排基于+844的压帮高度，内排基底3-1底板弱层完全破坏并回填岩石，破坏3-4底板弱层并回填岩石倾向长度达60 m时，内排土场及其与采场南帮复合边坡稳定性均可满足安全系数1.2要求。边坡稳定性随破坏底板弱层回填岩石范围的增大呈正指数函数规律提高，随着回填岩石范围长度的不断增加，边坡稳定性系数不断提高。采用破坏弱层回填岩石的基底处理方法，既保证了边坡的稳定又规避了过渡处理基底的生产成本。
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图 7 3-4煤层内排基底不同处理方式下边坡稳定性计算结果Figure 7. Calculation results of slope stability under different treatment methods of inner row basement (3-4)![]()
图 8 边坡稳定性与破坏弱层回填岩石范围关系曲线Figure 8. Relationship curve between slope stability and the extent of backfill rocks in the weak layer5. 结 论
（1） 弱层暴露长度是露天矿顺倾软岩边坡稳定性的主控因素，据此提出了露天矿顺倾软岩边坡内排追踪压帮治理工程，可最大限度的安全回收边坡压覆煤炭资源。
（2） 控制采场与内排土场间的追踪距离是改善边坡稳定性的有效途径。随着追踪距离的增加，边坡破坏模式从以圆弧为侧界面、弱层为底界面的切层-顺层-剪出滑动逐渐过渡为以圆弧为侧界面、弱层为底界面的切层-顺层滑动。
（3） 内排土场及其与采场构成的复合边坡稳定性随破坏底板弱层回填岩石范围的增大呈指数函数规律提高，随着回填岩石范围长度的不断增加，边坡稳定性系数不断提高。
（4） 贺斯格乌拉南露天煤矿首采区南帮浅部边坡留设40 m运输平盘、15 m保安平盘，底帮深部边坡角29°，追踪距离控制在50 m之内时可满足安全要求；内排基底弱层完全破坏并回填岩石倾向长度60 m时可满足安全需求。
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图 10 均方误差-树木数量关系图
Figure 10. Relationship between Mean Square Error and Number of Trees
表 1 各模型评价结果分区占比表
Table 1 Proportion of Landslide Zone Assessment Results for Each Model
极高/% 高/% 中/% 低/% LR 26.36 23.98 24.29 25.37 SVM 16.53 19.39 29.34 34.74 MLP 12.24 36.61 33.04 18.11 RF 20.16 25.58 20.75 33.51 
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[1] 吴宏阳,周超,梁鑫,等. 基于XGBoost模型的三峡库区燕山乡滑坡易发性评价与区划[J]. 中国地质灾害与防治学报,2023,34(5):141 − 152. [WU Hongyang,ZHOU Chao,LIANG Xin,et al. Assessment of landslide susceptibility mapping based on XGBoost model:a case study of Yanshan Township[J]. The Chinese Journal of Geological Hazard and Control,2023,34(5):141 − 152. (in Chinese with English abstract)] WU Hongyang, ZHOU Chao, LIANG Xin, et al. Assessment of landslide susceptibility mapping based on XGBoost model: a case study of Yanshan Township［J］. The Chinese Journal of Geological Hazard and Control, 2023, 34（5）: 141 − 152. （in Chinese with English abstract）
[2] 陈国金. 三峡库区巴东城区岸坡稳定性问题调查研究与风险控制[J]. 资源环境与工程,2023,37(3):292 − 300. [CHEN Guojin. Stability investigation and risk control of bank slope in Badong urban area,Three Gorges Reservoir area[J]. Resources Environment & Engineering,2023,37(3):292 − 300. (in Chinese with English abstract)] CHEN Guojin. Stability investigation and risk control of bank slope in Badong urban area, Three Gorges Reservoir area［J］. Resources Environment & Engineering, 2023, 37（3）: 292 − 300. （in Chinese with English abstract）
[3] 张曦,陈丽霞,徐勇,等. 两种斜坡单元划分方法对滑坡灾害易发性评价的对比研究[J]. 安全与环境工程,2018,25(1):12 − 17. [ZHANG Xi,CHEN Lixia,XU Yong,et al. Comparison of two methods for slope unit division in landslide susceptibility evaluation[J]. Safety and Environmental Engineering,2018,25(1):12 − 17. (in Chinese with English abstract)] ZHANG Xi, CHEN Lixia, XU Yong, et al. Comparison of two methods for slope unit division in landslide susceptibility evaluation［J］. Safety and Environmental Engineering, 2018, 25（1）: 12 − 17. （in Chinese with English abstract）
[4] 曾斌,吕权儒,寇磊,等. 基于Logistic回归和随机森林的清江流域长阳库岸段堆积层滑坡易发性评价[J]. 中国地质灾害与防治学报,2023,34(4):105 − 113. [ZENG Bin,LYU Quanru,KOU Lei,et al. Susceptibility assessment of colluvium landslides along the Changyang section of Qingjiang River using Logistic regression and random forest methods[J]. The Chinese Journal of Geological Hazard and Control,2023,34(4):105 − 113. (in Chinese with English abstract)] ZENG Bin, LYU Quanru, KOU Lei, et al. Susceptibility assessment of colluvium landslides along the Changyang section of Qingjiang River using Logistic regression and random forest methods［J］. The Chinese Journal of Geological Hazard and Control, 2023, 34（4）: 105 − 113. （in Chinese with English abstract）
[5] 王凯,张少杰,韦方强. 斜坡单元提取方法研究进展和展望[J]. 长江科学院院报,2020,37(6):85 − 93. [WANG Kai,ZHANG Shaojie,WEI Fangqiang. Slope unit extraction methods:advances and prospects[J]. Journal of Yangtze River Scientific Research Institute,2020,37(6):85 − 93. (in Chinese with English abstract)] DOI: 10.11988/ckyyb.20190210 WANG Kai, ZHANG Shaojie, WEI Fangqiang. Slope unit extraction methods: advances and prospects［J］. Journal of Yangtze River Scientific Research Institute, 2020, 37（6）: 85 − 93. （in Chinese with English abstract） DOI: 10.11988/ckyyb.20190210
[6] 刘军旗,刘强,刘千慧,等. 大数据时代地质灾害数据管理及应用模式探讨[J]. 地质科技通报,2021,40(6):276 − 282. [LIU Junqi,LIU Qiang,LIU Qianhui,et al. Discussion of geological hazard data management and application model in big data era[J]. Bulletin of Geological Science and Technology,2021,40(6):276 − 282. (in Chinese with English abstract)] LIU Junqi, LIU Qiang, LIU Qianhui, et al. Discussion of geological hazard data management and application model in big data era［J］. Bulletin of Geological Science and Technology, 2021, 40（6）: 276 − 282. （in Chinese with English abstract）
[7] 刘宝生,陈刚,程刚建. 江苏南京地质灾害风险评价[J]. 中国地质灾害与防治学报,2023,34(4):97 − 104. [LIU Baosheng,CHEN Gang,CHENG Gangjian. Risk assessment of geological disasters in Nanjing,Jiangsu Province[J]. The Chinese Journal of Geological Hazard and Control,2023,34(4):97 − 104. (in Chinese with English abstract)] LIU Baosheng, CHEN Gang, CHENG Gangjian. Risk assessment of geological disasters in Nanjing, Jiangsu Province［J］. The Chinese Journal of Geological Hazard and Control, 2023, 34（4）: 97 − 104. （in Chinese with English abstract）
[8] 王俊德,杜晓阳,黄天浩,等. 河南省嵩县地质灾害风险评价[J]. 中国地质灾害与防治学报,2023,34(4):86 − 96. [WANG Junde,DU Xiaoyang,HUANG Tianhao,et al. Risk assessment of geological hazards in Song County,Henan Province[J]. The Chinese Journal of Geological Hazard and Control,2023,34(4):86 − 96. (in Chinese with English abstract)] WANG Junde, DU Xiaoyang, HUANG Tianhao, et al. Risk assessment of geological hazards in Song County, Henan Province［J］. The Chinese Journal of Geological Hazard and Control, 2023, 34（4）: 86 − 96. （in Chinese with English abstract）
[9] 牟家琦,庄建琦,王世宝,等. 基于深度神经网络模型的雅安市滑坡易发性评价[J]. 中国地质灾害与防治学报,2023,34(3):157 − 168. [MU Jiaqi,ZHUANG Jianqi,WANG Shibao,et al. Evaluation of landslide susceptibility in Ya’an City based on depth neural network model[J]. The Chinese Journal of Geological Hazard and Control,2023,34(3):157 − 168. (in Chinese with English abstract)] MU Jiaqi, ZHUANG Jianqi, WANG Shibao, et al. Evaluation of landslide susceptibility in Ya’an City based on depth neural network model［J］. The Chinese Journal of Geological Hazard and Control, 2023, 34（3）: 157 − 168. （in Chinese with English abstract）
[10] 支泽民,刘峰贵,周强,等. 基于流域单元的地质灾害易发性评价——以西藏昌都市为例[J]. 中国地质灾害与防治学报,2023,34(1):139 − 150. [ZHI Zemin,LIU Fenggui,ZHOU Qiang,et al. Evaluation of geological hazards susceptibility based on watershed units:A case study of the Changdu City,Tibet[J]. The Chinese Journal of Geological Hazard and Control,2023,34(1):139 − 150. (in Chinese with English abstract)] ZHI Zemin, LIU Fenggui, ZHOU Qiang, et al. Evaluation of geological hazards susceptibility based on watershed units: A case study of the Changdu City, Tibet［J］. The Chinese Journal of Geological Hazard and Control, 2023, 34（1）: 139 − 150. （in Chinese with English abstract）
[11] 郭飞,王秀娟,陈玺,等. 基于不同模型的赣南地区小型削方滑坡易发性评价对比分析[J]. 中国地质灾害与防治学报,2022,33(6):125 − 133. [GUO Fei,WANG Xiujuan,CHEN Xi,et al. Comparative analyses on susceptibility of cutting slope landslides in southern Jiangxi using different models[J]. The Chinese Journal of Geological Hazard and Control,2022,33(6):125 − 133. (in Chinese with English abstract)] GUO Fei, WANG Xiujuan, CHEN Xi, et al. Comparative analyses on susceptibility of cutting slope landslides in southern Jiangxi using different models［J］. The Chinese Journal of Geological Hazard and Control, 2022, 33（6）: 125 − 133. （in Chinese with English abstract）
[12] 黄发明,殷坤龙,蒋水华,等. 基于聚类分析和支持向量机的滑坡易发性评价[J]. 岩石力学与工程学报,2018,37(1):156 − 167. [HUANG Faming,YIN Kunlong,JIANG Shuihua,et al. Landslide susceptibility assessment based on clustering analysis and support vector machine[J]. Chinese Journal of Rock Mechanics and Engineering,2018,37(1):156 − 167. (in Chinese with English abstract)] HUANG Faming, YIN Kunlong, JIANG Shuihua, et al. Landslide susceptibility assessment based on clustering analysis and support vector machine［J］. Chinese Journal of Rock Mechanics and Engineering, 2018, 37（1）: 156 − 167. （in Chinese with English abstract）
[13] WANG Jiaqi,SUN Pengfei,CHEN Leilei,et al. Recent advances of deep learning in geological hazard forecasting[J]. Computer Modeling in Engineering & Sciences,2023,137(2):1381 − 1418.
[14] 夏辉,殷坤龙,梁鑫,等. 基于SVM-ANN模型的滑坡易发性评价——以三峡库区巫山县为例[J]. 中国地质灾害与防治学报,2018,29(5):13 − 19. [XIA Hui,YIN Kunlong,LIANG Xin,et al. Landslide susceptibility assessment based on SVM-ANN Models:a case stualy for Wushan County in the Three Gorges Reservoir[J]. The Chinese Journal of Geological Hazard and Control,2018,29(5):13 − 19. (in Chinese with English abstract)] XIA Hui, YIN Kunlong, LIANG Xin, et al. Landslide susceptibility assessment based on SVM-ANN Models: a case stualy for Wushan County in the Three Gorges Reservoir［J］. The Chinese Journal of Geological Hazard and Control, 2018, 29（5）: 13 − 19. （in Chinese with English abstract）
[15] 何清,李宁,罗文娟,等. 大数据下的机器学习算法综述[J]. 模式识别与人工智能,2014,27(4):327 − 336. [HE Qing,LI Ning,LUO Wenjuan,et al. A survey of machine learning algorithms for big data[J]. Pattern Recognition and Artificial Intelligence,2014,27(4):327 − 336. (in Chinese with English abstract)] DOI: 10.3969/j.issn.1003-6059.2014.04.007 HE Qing, LI Ning, LUO Wenjuan, et al. A survey of machine learning algorithms for big data［J］. Pattern Recognition and Artificial Intelligence, 2014, 27（4）: 327 − 336. （in Chinese with English abstract） DOI: 10.3969/j.issn.1003-6059.2014.04.007
[16] YOUSSEF K,SHAO K,MOON S,et al. Landslide susceptibility modeling by interpretable neural network[J]. Communications Earth and Environment,2023,4(1):162. DOI: 10.1038/s43247-023-00806-5
[17] NACHAPPA T,GHORBANZADEH O,GHOLAMNIA K,et al. Multi-hazard exposure mapping using machine learning for the state of Salzburg,Austria[J]. Remote Sensing,2020,12(17):2757. DOI: 10.3390/rs12172757
[18] 马晨曦. 后三峡时期库区城市人居环境建设评价研究——以巴东、秭归为例[D]. 重庆:重庆大学,2019. [MA Chenxi. Evaluation of urban human settlement environment construction in the reservoir area in the post-three gorges period[D]. Chongqing:Chongqing University,2019. (in Chinese with English abstract)] MA Chenxi. Evaluation of urban human settlement environment construction in the reservoir area in the post-three gorges period［D］. Chongqing: Chongqing University, 2019. （in Chinese with English abstract）
[19] 左可顺. 高切坡数据集成与高效管理研究——以三峡湖北库区为例[D]. 中国地质大学(武汉),2023. [ZUO Keshun. Research on Data Integration and Efficient Management of High Cutting Slope--Taking Three Gorges Hubei Reservoir Area as an Example[D]. China University of Geosciences (Wuhan),2023. (in Chinese)] ZUO Keshun. Research on Data Integration and Efficient Management of High Cutting Slope--Taking Three Gorges Hubei Reservoir Area as an Example［D］. China University of Geosciences （Wuhan）, 2023. （in Chinese）
[20] 石菊松,张永双,董诚,等. 基于GIS技术的巴东新城区滑坡灾害危险性区划[J]. 地球学报,2005,26(3):275 − 282. [SHI Jusong,ZHANG Yongshuang,DONG Cheng,et al. GIS-based landslide hazard zonation of the new Badong County site[J]. Acta Geosicientia Sinica,2005,26(3):275 − 282. (in Chinese with English abstract)] DOI: 10.3321/j.issn:1006-3021.2005.03.014 SHI Jusong, ZHANG Yongshuang, DONG Cheng, et al. GIS-based landslide hazard zonation of the new Badong County site［J］. Acta Geosicientia Sinica, 2005, 26（3）: 275 − 282. （in Chinese with English abstract） DOI: 10.3321/j.issn:1006-3021.2005.03.014
[21] 王瑛,林齐根,史培军. 中国地质灾害伤亡事件的空间格局及影响因素[J]. 地理学报,2017,72(5):906 − 917. [WANG Ying,LIN Qigen,SHI Peijun. Spatial pattern and influencing factors of casualty events caused by landslides[J]. Acta Geographica Sinica,2017,72(5):906 − 917. (in Chinese with English abstract)] DOI: 10.11821/dlxb201705011 WANG Ying, LIN Qigen, SHI Peijun. Spatial pattern and influencing factors of casualty events caused by landslides［J］. Acta Geographica Sinica, 2017, 72（5）: 906 − 917. （in Chinese with English abstract） DOI: 10.11821/dlxb201705011
[22] 吴润泽,程温鸣,刘军旗,等. 三峡库区地质灾害防治信息系统及预警指挥系统数据管理模式探讨[J]. 中国地质灾害与防治学报,2018,29(5):102 − 107. [WU Runze,CHENG Wenming,LIU Junqi,et al. Discussion on the data management mode of geologic disaster prevention and control information system and early warning command system in the Three Gorges Reservoir Area[J]. The Chinese Journal of Geological Hazard and Control,2018,29(5):102 − 107. (in Chinese with English abstract)] WU Runze, CHENG Wenming, LIU Junqi, et al. Discussion on the data management mode of geologic disaster prevention and control information system and early warning command system in the Three Gorges Reservoir Area［J］. The Chinese Journal of Geological Hazard and Control, 2018, 29（5）: 102 − 107. （in Chinese with English abstract）
[23] LIU Junqi,HUANG Xuebin,WU Chonglong,et al. From the area to the point-study on the key technology of 3D geological hazard modeling in Three Gorges Reservoir area[J]. Journal of Earth Science,2012,23(2):199 − 206. DOI: 10.1007/s12583-012-0246-5
[24] 刘越凡,付萧,朱庆,等. 顾及地貌形态特征的精细斜坡单元高效分区划分[J]. 测绘科学,2023,48(4):211 − 220. [LIU Yuefan,FU Xiao,ZHU Qing,et al. An efficient and fine slope units division method with consideration of the regional geomorphological characteristics[J]. Science of Surveying and Mapping,2023,48(4):211 − 220. (in Chinese with English abstract)] LIU Yuefan, FU Xiao, ZHU Qing, et al. An efficient and fine slope units division method with consideration of the regional geomorphological characteristics［J］. Science of Surveying and Mapping, 2023, 48（4）: 211 − 220. （in Chinese with English abstract）
[25] HUANG Faming,CAO Zhongshan,JIANG Shuihua,et al. Landslide susceptibility prediction based on a semi-supervised multiple-layer perceptron model[J]. Landslides,2020,17(12):2919 − 2930. DOI: 10.1007/s10346-020-01473-9
[26] 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 Lina,XU Chong,XU Xiwei,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. (in Chinese with English abstract)] DOI: 10.3969/j.issn.1000-811X.2014.04.034 LIU Lina, XU Chong, XU Xiwei, 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. （in Chinese with English abstract） DOI: 10.3969/j.issn.1000-811X.2014.04.034
[27] 李松林,许强,汤明高,等. 三峡库区滑坡空间发育规律及其关键影响因子[J]. 地球科学,2020,45(1):341 − 354. [LI Songlin,XU Qiang,TANG Minggao,et al. Study on spatial distribution and key influencing factors of landslides in Three Gorges Reservoir area[J]. Earth Science,2020,45(1):341 − 354. (in Chinese with English abstract)] LI Songlin, XU Qiang, TANG Minggao, et al. Study on spatial distribution and key influencing factors of landslides in Three Gorges Reservoir area［J］. Earth Science, 2020, 45（1）: 341 − 354. （in Chinese with English abstract）
[28] 石菊松,徐瑞春,石玲,等. 基于RS和GIS技术的清江隔河岩库区滑坡易发性评价与制图[J]. 地学前缘,2007,14(6):119 − 128. [SHI Jusong,XU Ruichun,SHI Ling,et al. ETM+ imagery and GIS-based landslide susceptibility mapping for the regional area of Geheyan Reservoir on the Qingjiang River,Hubei Province,China[J]. Earth Science Frontiers,2007,14(6):119 − 128. (in Chinese with English abstract)] DOI: 10.3321/j.issn:1005-2321.2007.06.015 SHI Jusong, XU Ruichun, SHI Ling, et al. ETM+ imagery and GIS-based landslide susceptibility mapping for the regional area of Geheyan Reservoir on the Qingjiang River, Hubei Province, China［J］. Earth Science Frontiers, 2007, 14（6）: 119 − 128. （in Chinese with English abstract） DOI: 10.3321/j.issn:1005-2321.2007.06.015
[29] WANG Jinge,XIANG Wei,LU Ning. Landsliding triggered by reservoir operation:a general conceptual model with a case study at Three Gorges Reservoir[J]. Acta Geotechnica,2014,9(5):771 − 788. DOI: 10.1007/s11440-014-0315-2
[30] 郭惠娟,唐南奇,林金宝. 基于GIS的仙游县土地利用与滑坡灾害敏感性分析[J]. 福建农林大学学报(自然科学版),2010,39(4):417 − 420. [GUO Huijuan,TANG Nanqi,LIN Jinbao. Sensibility analysis of land-use and landslide hazard based on GIS in Xianyou County[J]. Journal of Fujian Agriculture and Forestry University (Natural Science Edition),2010,39(4):417 − 420. (in Chinese with English abstract)] GUO Huijuan, TANG Nanqi, LIN Jinbao. Sensibility analysis of land-use and landslide hazard based on GIS in Xianyou County［J］. Journal of Fujian Agriculture and Forestry University （Natural Science Edition）, 2010, 39（4）: 417 − 420. （in Chinese with English abstract）
[31] 章昱,王磊,伏永朋,等. 基于斜坡单元与信息量法的丹江口库区典型流域地质灾害易发性评价[J]. 华南地质,2023,39(3):512 − 522. [ZHANG Yu,WANG Lei,FU Yongpeng,et al. Evaluation of geological disaster susceptibility in typical watershed of Danjiangkou Reservoir area based on slope unit and information method[J]. South China Geology,2023,39(3):512 − 522. (in Chinese with English abstract)] DOI: 10.3969/j.issn.2097-0013.2023.03.010 ZHANG Yu, WANG Lei, FU Yongpeng, et al. Evaluation of geological disaster susceptibility in typical watershed of Danjiangkou Reservoir area based on slope unit and information method［J］. South China Geology, 2023, 39（3）: 512 − 522. （in Chinese with English abstract） DOI: 10.3969/j.issn.2097-0013.2023.03.010
[32] 刘亮,杨洋. 基于网格单元和随机森林的滑坡易发性评价[J]. 石化技术,2023,30(6):180 − 182. [LIU Liang,YANG Yang. Landslide susceptibility evaluation based on grid cells and random forest[J]. Petrochemical Industry Technology,2023,30(6):180 − 182. (in Chinese with English abstract)] DOI: 10.3969/j.issn.1006-0235.2023.06.060 LIU Liang, YANG Yang. Landslide susceptibility evaluation based on grid cells and random forest［J］. Petrochemical Industry Technology, 2023, 30（6）: 180 − 182. （in Chinese with English abstract） DOI: 10.3969/j.issn.1006-0235.2023.06.060
[33] 陈刚,郝社锋,蒋波等. 基于机载LiDAR技术植被茂密区小型滑坡识别与评价[J/OL]. 自然资源遥感,2023,1 − 10. [CHEN Gang,HE Shefeng,JIANG Bo,et al. Identification and evaluation of small landslides in densely vegetated areas based on airborne LiDAR technology [J/OL]. Remote Sensing of Natural Resources,2023,1 − 10. (in Chinese with English abstract)] CHEN Gang, HE Shefeng, JIANG Bo, et al. Identification and evaluation of small landslides in densely vegetated areas based on airborne LiDAR technology ［J/OL］. Remote Sensing of Natural Resources, 2023, 1 − 10. （in Chinese with English abstract）
[34] 张玺国,周雄冬,徐梦珍,等. 西藏地质灾害易发性及对水能开发适宜度影响[J]. 地理学报,2022,77(7):1603 − 1614. [ZHANG Xiguo,ZHOU Xiongdong,XU Mengzhen,et al. Distribution of hydropower development suitability in Tibet in the face of geological hazard susceptibility[J]. Acta Geographica Sinica,2022,77(7):1603 − 1614. (in Chinese with English abstract)] DOI: 10.11821/dlxb202207003 ZHANG Xiguo, ZHOU Xiongdong, XU Mengzhen, et al. Distribution of hydropower development suitability in Tibet in the face of geological hazard susceptibility［J］. Acta Geographica Sinica, 2022, 77（7）: 1603 − 1614. （in Chinese with English abstract） DOI: 10.11821/dlxb202207003
[35] 邹浩,贾琳,郑路路等. 基于覆盖土层厚度识别的区域斜坡降雨入渗稳定性定量评价[J/OL]. 地球科学,2023,1 − 14. [Zhou Hao,Jia Lin,Zheng Lulu,et al. Quantitative evaluation of rainfall infiltration stability on regional slopes based on overburden soil thickness identification[J/OL]. Earth Science,2023,1 − 14. (in Chinese with English abstract)] Zhou Hao, Jia Lin, Zheng Lulu, et al. Quantitative evaluation of rainfall infiltration stability on regional slopes based on overburden soil thickness identification［J/OL］. Earth Science, 2023, 1 − 14. （in Chinese with English abstract）
[36] 丁世飞,齐丙娟,谭红艳. 支持向量机理论与算法研究综述[J]. 电子科技大学学报,2011,40(1):2 − 10. [DING Shifei,QI Bingjuan,TAN Hongyan. An overview on theory and algorithm of support vector machines[J]. Journal of University of Electronic Science and Technology of China,2011,40(1):2 − 10. (in Chinese with English abstract)] DING Shifei, QI Bingjuan, TAN Hongyan. An overview on theory and algorithm of support vector machines［J］. Journal of University of Electronic Science and Technology of China, 2011, 40（1）: 2 − 10. （in Chinese with English abstract）
[37] WANG Sen,LING Sixiang,WU Xiyong,et al. Key predisposing factors and susceptibility assessment of landslides along the Yunnan–Tibet traffic corridor,Tibetan Plateau:comparison with the LR,RF,NB,and MLP techniques[J]. Frontiers in Earth Science,2023,10:1100363. DOI: 10.3389/feart.2022.1100363
[38] YU Haiwei,PEI Wenjie,ZHANG Jingyi,et al. Landslide susceptibility mapping and driving mechanisms in a vulnerable region based on multiple machine learning models[J]. Remote Sensing,2023,15(7):1886. DOI: 10.3390/rs15071886
[39] RAGHU S,SRIRAAM N. Optimal configuration of multilayer perceptron neural network classifier for recognition of intracranial epileptic seizures[J]. Expert Systems with Applications,2017,89(C):205 − 221.
[40] TASER P Y. Application of bagging and boosting approaches using decision tree-based algorithms in diabetes risk prediction[C]//The 7th International Management Information Systems Conference. Basel Switzerland:MDPI,2021,89(C):205 − 221.
[41] 窦杰,向子林,许强,等. 机器学习在滑坡智能防灾减灾中的应用与发展趋势[J]. 地球科学,2023,48(5):1657 − 1674. [DOU Jie,XIANG Zilin,XU Qiang,et al. Application and development trend of machine learning in landslide intelligent disaster prevention and mitigation[J]. Earth Science,2023,48(5):1657 − 1674. (in Chinese with English abstract)] DOU Jie, XIANG Zilin, XU Qiang, et al. Application and development trend of machine learning in landslide intelligent disaster prevention and mitigation［J］. Earth Science, 2023, 48（5）: 1657 − 1674. （in Chinese with English abstract）
[42] 孙滨,祝传兵,康晓波,等. 基于信息量模型的云南东川泥石流易发性评价[J]. 中国地质灾害与防治学报,2022,33(5):119 − 127. [SUN Bin,ZHU Chuanbing,KANG Xiaobo,et al. Susceptibility assessment of debris flows based on information model in Dongchuan,Yunnan Province[J]. The Chinese Journal of Geological Hazard and Control,2022,33(5):119 − 127. (in Chinese with English abstract)] SUN Bin, ZHU Chuanbing, KANG Xiaobo, et al. Susceptibility assessment of debris flows based on information model in Dongchuan, Yunnan Province［J］. The Chinese Journal of Geological Hazard and Control, 2022, 33（5）: 119 − 127. （in Chinese with English abstract）
[43] NAMAN KAUR,HIMANSHU. Logistic regression:A basic approach[J]. Information and Communication Technol- ogy for Competitive Strategies,2022,623:481 − 488.
[44] 吴润泽,胡旭东,梅红波,等. 基于随机森林的滑坡空间易发性评价——以三峡库区湖北段为例[J]. 地球科学,2021,46(1):321 − 330. [WU Runze,HU Xudong,MEI Hongbo,et al. Spatial susceptibility assessment of landslides based on random forest:A case study from Hubei section in the Three Gorges Reservoir area[J]. Earth Science,2021,46(1):321 − 330. (in Chinese with English abstract)] WU Runze, HU Xudong, MEI Hongbo, et al. Spatial susceptibility assessment of landslides based on random forest: A case study from Hubei section in the Three Gorges Reservoir area［J］. Earth Science, 2021, 46（1）: 321 − 330. （in Chinese with English abstract）
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