Reservoir Landslide Susceptibility Prediction Considering Non-Landslide Sampling and Ensemble Machine Learning Methods
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摘要: 准确的滑坡易发性建模对预警预报和风险管控具有重要意义.针对机器学习技术建模中非滑坡样本随机选取和单个分类器存在的精度不高问题，提出了一种耦合多模型的区域滑坡易发性建模框架.以三峡库区秭归-巴东段为例，选取高程、坡度等12个因子构建评价指标体系，应用信息量法定量分析各指标对滑坡空间发育的影响程度.随机选取70%的滑坡作为训练样本，剩余的30%作为验证样本；应用逻辑回归模型（LR）制作研究区的初始易发性分区图，确定非滑坡随机采样的约束范围.随后，分别采用LR模型约束和无约束条件下随机选取的非滑坡样本，应用单个分类回归树（LR-CART和No-CART）及分类回归树-Bagging组合模型（LR-CART-Bagging和No-CART-Bagging）开展滑坡易发性建模，并应用多个指标进行精度评估.结果发现：高程和水系等是滑坡发育的主控因素；LR-CART-Bagging模型精度为0.973，高于LR-CART模型的0.889；相比于No-CART和No-CART-Bagging模型，LR-CART和LR-CART-Bagging模型精度分别提升了0.057和0.047.LR模型可以有效约束非滑坡样本的选取范围，提升样本的选取质量；CART-Bagging模型综合了机器学习和集成学习的优势，预测性能更强，提出的LR-CART-Bagging模型是一种准确可靠的滑坡易发性建模方法.Abstract: Landslide susceptibility evaluation is important for its early warning and forecasting and risk management.To address the problems of a random selection of non-landslide samples and low accuracy of individual classifiers in modeling by machine learning techniques, a coupled multi-model regional landslide susceptibility modeling framework is proposed.Taking the Zigui-Badong section of the Three Gorges reservoir area as an example, 12 factors such as elevation and slope were selected to construct an evaluation index system, and the information quantity method was applied to quantify the influence degree of each factor on landslide spatial development. 70% of the landslides were randomly selected as training samples and the remaining 30% as validation samples; the Logistic Regression model (LR) was applied to produce an initial susceptibility zoning map of the study area and to determine the constraint range for random sampling of non-landslides. Subsequently, a single Classification and Regression Tree (LR-CART and No-CART) and combined Classification and Regression Tree-Bagging model (LR-CART-Bagging and No-CART-Bagging) were applied to model landslide susceptibility using randomly selected non-landslide samples under the constrained and unconstrained conditions of LR model, respectively, and multiple metrics were applied for accuracy assessment.The results show that elevation and water system are the main controlling factors for landslide development; the accuracy of the LR-CART-Bagging model is 0.973, higher than 0.889 of the LR-CART model; compared with No-CART and No-CART-Bagging models, the accuracy of LR-CART and LR-CART-Bagging models is improved by 0.057 and 0.047, respectively.LR model can effectively constrain the selection range of non-landslide samples and improve the quality of sample selection; the CART-Bagging model integrates the advantages of machine learning and ensemble learning with better prediction performance, and the proposed LR-CART-Bagging model is an accurate and reliable method for landslide susceptibility modeling.
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图 1 研究区高程与滑坡灾害分布
Fig. 1. Elevation and landslide disaster distributions in the study area
图 2 易发性制图技术路线
Fig. 2. Flowchart of the proposed landslide susceptibility mapping method
图 5 易发性评价指标
a.高程（m）；b.坡度（°）；c.坡向（°）；d.地表粗糙度；e.地形起伏度（m）；f.斜坡形态；g.土地利用类型；h.斜坡结构；i.地层岩性；j.地形湿度指数；k.水系距离（m）；l.构造距离（m）
Fig. 5. Index chart of susceptibility evaluation
图 6 初始易发性图和非滑坡样本分布
Fig. 6. Original susceptibility map and non-landslide samples distribution
图 7 易发性分区
Fig. 7. Landslide susceptibility maps
a. LR-CART-Bagging; b. LR-CART; c. No-CART-Bagging; d. No-CART
表 1 易发性影响因素信息量值
Table 1. Information value of conditioning factors
评价指标 状态分级 信息量I/bit 评价指标 状态分级 信息量I/bit 坡度（°） 0~9 ‒0.90 坡向 平 ‒1.93 9~18 0.37 北 0.47 18~27 0.42 东北 0.23 27~36 ‒0.28 东 ‒0.27 > 36 ‒1.63 东南 ‒0.31 地表粗糙度 1~1.1 0.37 南 0.43 1.1~1.2 ‒0.04 西南 ‒0.29 1.2~1.3 ‒0.88 西 ‒0.71 1.3~1.4 ‒1.90 西北 ‒0.03 1.4~1.5 ‒2.48 构造距离（m） 0~500 ‒0.04 > 1.5 ‒2.49 500~1 000 0.08 水系距离（m） 0~300 0.92 1 000~1 500 0.24 300~600 0.29 1 500~2 000 0.23 600~900 ‒0.57 > 2 000 ‒0.22 900~1 200 ‒1.70 高程（m） < 240 1.49 > 1 200 ‒2.95 240~450 0.54 地层岩性 L1 ‒3.94 450~650 ‒1.36 L2 ‒0.3 650~1 200 ‒3.84 L3 0.17 > 1 200 ‒∞ L4 ‒0.34 斜坡形态 X/X 0.16 L5 0.42 X/V ‒0.96 地形湿度指数 1.37~3 ‒2.49 X/GE ‒1.45 3~4.5 ‒0.37 V/X 0.04 4.5~6 ‒0.20 V/V ‒1.74 6~7.5 0.47 V/GE ‒1.19 7.5~9 0.72 GR/X 0.01 > 9 0.02 GR/V ‒1.02 地形起伏度（m） 0~14 ‒0.63 GR/GE ‒1.46 14~35 0.47 斜坡结构 B1 ‒∞ 35~42 0.08 B2 0.14 42~49 ‒0.47 B4 0.13 > 49 ‒1.70 B5 0.08 土地利用类型 山地 ‒0.59 B6 ‒0.17 耕地 0.12 B7 ‒0.34 水体 0.43 B8 ‒0.67 建设用地 0.85 注：地层岩性、斜坡结构和斜坡形态的缩写含义请查看表 2~表 4. 
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表 2 斜坡形态划分依据（彭令, 2013）
Table 2. Basis for slope type classification (Peng, 2013)
剖面曲率
平面曲率凸形坡
(X)凹形坡
(V)直线坡
(GE)外向形坡（X） X/X X/V X/GE 内向形坡（V） V/X V/V V/GE 直坡（GR） GR/X GR/V GR/GE
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表 3 斜坡结构分类
Table 3. Classification of slope structure
类别 定义（坡度：θ，坡向：σ，地层倾角：α，岩层倾角：β） 近水平层面坡（B1） α < 5° 顺向飘倾坡（B2） (|α-β|)∈(0, 30°]||(|α-β|)∈(330°, 360°]&&（α > 5°）&&（θ > α） 顺向层面坡（B3） (|α-β|)∈(0, 30°]||(|α-β|)∈(330°, 360°]&&（α > 5°）&&（θ=α） 顺向伏倾坡（B4） (|α-β|)∈(0, 30°]||(|α-β|)∈(330°, 360°]&&（α > 5°）&&（θ < α） 顺斜坡（B5） (|α-β|)∈ [30°, 60°)||(|α-β|)∈[300°, 330°) 横向坡（B6） (|α-β|)∈ [60°, 120°)||(|α-β|)∈[240°, 300°) 逆斜坡（B7） (|α-β|)∈ [90°, 150°)||(|α-β|)∈[210°, 240°) 逆向坡（B8） (|α-β|)∈ [150°, 180°)||(|α-β|)∈[180°, 210°)
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表 4 地层岩性分类
Table 4. Stratigraphic lithology classification
类型 分布地层 主要岩性 L1 δ22‒1、Pt 花岗岩、闪长岩 L2 Z、ε1、ε2+3、O、T1j、T2b3 灰岩、页岩、砂岩 L3 T1d、T2b4+5、J1x、J2s、J3s 泥灰岩、泥岩 L4 S、J2x 页岩，泥岩与石英砂岩，泥质粉砂岩等 L5 T3s、J1‒2n、J3p 砂岩（长石砂岩、石英砂岩等）夹煤层
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表 5 评价指标共线性分析
Table 5. Multi-collinearity analysis of contributing factors
评价指标 T VIF 高程 0.43 2.30 坡度 0.26 3.80 坡向 0.96 1.04 地表粗糙度 0.31 3.18 地形起伏度 0.32 3.12 斜坡形态 0.64 1.52 土地利用类型 0.81 1.22 斜坡结构 0.26 3.80 地层岩性 0.94 1.06 地形湿度指数 0.75 1.34 水系距离 0.45 2.25 断层距离 0.95 1.05
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表 6 不同树深与子模型数量下的精度统计
Table 6. Accuracy statistics under different tree depths and numbers of submodels
4 6 8 10 12 4 0.947 0.955 0.957 0.957 0.955 5 0.948 0.955 0.957 0.956 0.956 6 0.950 0.955 0.959 0.963 0.962 7 0.950 0.955 0.961 0.967 0.965 8 0.950 0.958 0.963 0.968 0.966 9 0.952 0.968 0.969 0.970 0.968 10 0.952 0.960 0.973 0.967 0.967 11 0.954 0.955 0.969 0.967 0.957 12 0.954 0.955 0.961 0.966 0.957
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表 7 易发性分区统计结果
Table 7. Statistical results of susceptibility zoning
模型 易发性等级 分区内滑坡栅格数a 滑坡百分比b(%) 分区栅格数c 栅格百分比d(%) 滑坡比率（b/d） No-CART 极低易发区 861 3.56 353 136 50.55 0.07 低易发区 1 506 6.22 138 069 19.76 0.31 中易发区 4 882 20.18 101 150 14.48 1.39 高易发区 8 393 34.69 69 174 9.90 3.50 极高易发区 8 553 35.35 37 117 5.31 6.65 No-CART-Bagging 极低易发区 622 2.57 342 258 48.99 0.05 低易发区 1 340 5.54 145 233 20.79 0.26 中易发区 3 750 15.50 107 928 15.45 1.00 高易发区 8 688 35.91 67 105 9.61 3.74 极高易发区 9 795 40.48 36 122 5.17 7.83 LR-CART 极低易发区 645 2.67 341 817 48.93 0.05 低易发区 1 543 6.38 145 048 20.76 0.30 中易发区 4 674 19.32 107 442 15.38 1.26 高易发区 8 726 36.07 68 256 9.77 3.69 极高易发区 8 607 35.57 36 083 5.16 6.89 LR-CART-Bagging 极低易发区 413 1.71 342 258 48.99 0.03 低易发区 1 105 4.57 145 233 20.79 0.22 中易发区 3 786 15.65 107 928 15.45 1.01 高易发区 8 883 36.71 67 105 9.61 3.82 极高易发区 10 008 41.36 36 122 5.17 8.00
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