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滑坡易发性评价中样本不均衡问题处理研究

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田尤, 高波, 殷红, 李元灵, 张佳佳, 陈龙, 李洪梁. 滑坡易发性评价中样本不均衡问题处理研究[J]. 水文地质工程地质, 2024, 51(6): 171-181. doi: 10.16030/j.cnki.issn.1000-3665.202307002
引用本文: 田尤, 高波, 殷红, 李元灵, 张佳佳, 陈龙, 李洪梁. 滑坡易发性评价中样本不均衡问题处理研究[J]. 水文地质工程地质, 2024, 51(6): 171-181. doi: 10.16030/j.cnki.issn.1000-3665.202307002
shu
TIAN You, GAO Bo, YIN Hong, LI Yuanling, ZHANG Jiajia, CHEN Long, LI Hongliang. Handling imbalanced samples in landslide susceptibility evaluation[J]. Hydrogeology & Engineering Geology, 2024, 51(6): 171-181. doi: 10.16030/j.cnki.issn.1000-3665.202307002
Citation: TIAN You, GAO Bo, YIN Hong, LI Yuanling, ZHANG Jiajia, CHEN Long, LI Hongliang. Handling imbalanced samples in landslide susceptibility evaluation[J]. Hydrogeology & Engineering Geology, 2024, 51(6): 171-181. doi: 10.16030/j.cnki.issn.1000-3665.202307002
shu

滑坡易发性评价中样本不均衡问题处理研究

  • 1.

    中国地质科学院探矿工艺研究所，四川 成都　611734

  • 2.

    自然资源部地质灾害风险防控工程技术创新中心，四川 成都　611734

  • 3.

    四川省地质灾害防治工程技术研究中心，四川 成都　610081

  • 4.

    四川省地质环境调查研究中心，四川 成都　610081

  • 基金项目: 中国地质调查局地质调查项目（DD20230449；DD20190644）；第二次青藏高原综合科学考察研究项目（2019QZKK0902）
详细信息
    作者简介: 田尤（1991—），男，硕士，工程师，主要从事地质灾害调查与评价研究工作。E-mail：tianyou2013@yeah.net

  • 中图分类号: P642.22

Handling imbalanced samples in landslide susceptibility evaluation

  • 1.

    Institute of Exploration Technology, Chinese Academy of Geological Sciences, Chengdu, Sichuan　611734, China

  • 2.

    Technology Innovation Center for Risk Prevention and Mitigation of Geohazard, Ministry of Natural Resources, Chengdu, Sichuan　611734, China

  • 3.

    Sichuan Province Engineering Technology Research Center of Geohazard Prevention, Chengdu, Sichuan　610081, China

  • 4.

    Sichuan Geological Environment Survey and Research Center, Chengdu, Sichuan　610081, China

    摘要

  • 滑坡易发性评价中，样本不均衡问题的不同处理方案通常会带来评价结果的大量不确定性。针对这一问题，以藏东昌都市部分县（区）为研究区，构建滑坡/非滑坡样本不均衡数据集，采用不处理、下采样和合成少数类过采样（synthetic minority oversampling technique, SMOTE）3种处置方案，运用逻辑回归方法分别构建滑坡易发性评价模型。基于ROC曲线、准确度、精确率、召回率、漏检率等评价指标，采用综合评价指标F1′同数对模型分类的精度进行验证。结果表明：数据处理成均衡数据集（过采样/下采样）建立的模型效果较不处理数据建立的模型效果有了大幅提升，F1′同数的值最大提高了53.17%；在下采样、过采样两种数据处理方案中，过采样方法比下采样方法F1′分数的值提高了16.30%，表明过采样方法对处理样本不均衡数据问题方面具有较好效果。研究成果可为滑坡预测和地质灾害预测前的数据集处理提供参考，为进一步提高区域防灾减灾水平提供理论与技术支持。

    In landslide susceptibility assessment, different approaches to handling sample imbalance can introduce significant uncertainty in evaluation outcomes. To address this issue, this study focused on the Changdu area of eastern Tibet and constructed the landslide susceptibility evaluation model using a dataset with imbalanced landslide and non-landslide samples. Three disposal schemes were applied: no treatment, downsampling, and SMOTE oversampling. The logistic regression method was used to construct the landslide susceptibility evaluation model. Based on ROC curve, accuracy, precision, recall, missed detection rate, and other evaluation indicators, the comprehensive evaluation index of F1′ score was used to verify the accuracy of model classification. The results show that the modeling effect of landslide susceptibility obtained by data processing into equilibrium data (downsampling/oversampling) is greatly improved compared with that obtained without processing data. Specifically, the value of the F1′score of the comprehensive index was increased by 53.17%. In the two schemes for processing data (downsampling and oversampling), the oversampling method increased the value of the composite index F1′ score by 16.30% compared with the downsampling method, indicating that the oversampling method has effectiveness in handling unbalanced data. This study can provide basic information for processing of data sets before landslide prediction and geological disaster prediction, and provide theoretical and technical support for further improving regional disaster prevention and mitigation.

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  • 图 1  研究技术框架图

    Figure 1. 

    下载: 全尺寸图片 幻灯片

    图 2  混淆矩阵

    Figure 2. 

    下载: 全尺寸图片 幻灯片

    图 3  研究区位置及滑坡分布图

    Figure 3. 

    下载: 全尺寸图片 幻灯片

    图 4  评价因子

    Figure 4. 

    下载: 全尺寸图片 幻灯片

    图 5  基于频率比法的研究区易发性评价结果

    Figure 5. 

    下载: 全尺寸图片 幻灯片

    图 6  三种处置方案的ROC曲线对比

    Figure 6. 

    下载: 全尺寸图片 幻灯片

    图 7  各处理方法验证结果混淆矩阵

    Figure 7. 

    下载: 全尺寸图片 幻灯片

    表 1  各评价因子分级及频率比值

    Table 1.  Frequency ratio of each evaluation factor

    指标因子 指标分级 占滑坡
    栅格比/%    		 占总
    栅格比/%    		 频率比 归一化值 指标因子 指标分级 占滑坡
    栅格比/%    		 占总
    栅格比/%    		 频率比 归一化值
    坡度/（°） [0, 10) 0.05 0.09 0.55 0 坡向 西南 0.13 0.14 0.95 0.66
    [10, 17) 0.09 0.13 0.71 0.07 西 0.13 0.12 1.00 0.74
    [17, 23) 0.13 0.16 0.81 0.12 西北 0.07 0.11 0.60 0.15
    [23, 28) 0.18 0.18 1.03 0.22 距断层
    距离/m    		 [0, 500) 0.15 0.14 1.04 0.55
    [28, 33) 0.18 0.18 0.98 0.19 [500, 1000) 0.12 0.13 0.90 0.12
    [33, 39) 0.17 0.15 1.08 0.24 [1000, 2 000) 0.22 0.22 0.99 0.41
    [39, 47) 0.15 0.09 1.66 0.50 [2 000, 4000) 0.22 0.26 0.86 0
    [47, 81] 0.06 0.02 2.79 1.00 ≥4000 0.29 0.25 1.18 1.00
    高程/m [2496, 3435) 0.34 0.03 10.53 1.00 工程地质
    岩组    		 较坚硬层状碎屑岩组 0.13 0.19 0.68 0.25
    [3435, 3787) 0.26 0.07 3.58 0.34 较坚硬层状碳酸盐岩组 0.10 0.12 0.89 0.52
    [3787, 4055) 0.21 0.12 1.73 0.16 软硬相间互层状碎屑岩组 0.48 0.41 1.16 0.86
    [4055, 4278) 0.12 0.17 0.71 0.07 坚硬块状侵入岩组 0.15 0.13 1.16 0.85
    [4278, 4487) 0.05 0.21 0.24 0.02 较软弱薄层浅变质岩组 0.06 0.08 0.73 0.31
    [4487, 4705) 0.02 0.19 0.12 0.01 坚硬厚层-块状深变质岩组 0.07 0.05 1.27 1.00
    [4705, 4966) 0 0.14 0.02 0 第四系松散岩组 0.01 0.02 0.49 0
    [4966, 5784] 0 0.07 0.02 0 距河流
    距离/m    		 [0, 500) 0.72 0.23 3.09 1.00
    曲率 [−9.23, −1.25) 0.07 0.03 2.55 1.00 [500, 1000) 0.13 0.21 0.62 0.18
    [−1.25, −0.65) 0.16 0.10 1.60 0.46 [1000, 1500) 0.08 0.19 0.42 0.11
    [−0.65, −0.28) 0.21 0.18 1.13 0.20 [1500, 2 000) 0.04 0.16 0.28 0.06
    [−0.28, 0.09) 0.19 0.24 0.78 0 [2 000, 2500) 0.02 0.12 0.19 0.03
    [0.09, 0.46) 0.18 0.22 0.86 0.04 ≥2500 0.01 0.10 0.10 0
    [0.46, 0.90) 0.12 0.14 0.82 0.02 植被指数 [0, 0.10) 0.01 0.09 0.07 0
    [0.90, 1.57) 0.06 0.08 0.81 0.02 [0.10, 0.21) 0.06 0.07 0.85 0.45
    [1.57, 9.63] 0.02 0.02 0.98 0.11 [0.21, 0.30) 0.23 0.13 1.79 1.00
    坡向 平面 0 0 0.50 0 [0.30, 0.37) 0.30 0.22 1.35 0.74
    北 0.10 0.12 0.83 0.49 [0.37, 0.43) 0.21 0.23 0.90 0.48
    东北 0.17 0.15 1.17 0.98 [0.43, 0.51) 0.11 0.15 0.71 0.37
    东 0.14 0.13 1.06 0.83 [0.51, 0.62) 0.06 0.08 0.73 0.38
    东南 0.13 0.11 1.18 1.00 [0.62, 1] 0.03 0.03 1.14 0.62
    南 0.13 0.11 1.18 1.00
    下载: 导出CSV

    表 2  不同处理方式得到的逻辑回归模型预测结果评价

    Table 2.  Evaluation of logistic regression model prediction results obtained by different processing methods

    评价指标
    处理方式    		 准确度/% 精确率/% 召回率/% 漏检率/%
    不处理 99.16 60.43 24.96 75.04
    下采样 82.25 91.57 71.95 28.05
    过采样 95.78 16.76 90.48 9.52
    下载: 导出CSV
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收稿日期:  2023-07-02
修回日期:  2023-11-07
录用日期:  2023-11-09
刊出日期:  2024-11-15

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