小样本机器学习方法在新疆滑坡灾害易发性评价中的应用

梁龙飞

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梁龙飞. 2023. 小样本机器学习方法在新疆滑坡灾害易发性评价中的应用[J].工程地质学报, 31(4): 1394-1406. doi: 10.13544/j.cnki.jeg.2023-0226
引用本文: 梁龙飞. 2023. 小样本机器学习方法在新疆滑坡灾害易发性评价中的应用[J].工程地质学报, 31(4): 1394-1406. doi: 10.13544/j.cnki.jeg.2023-0226
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Liang Longfei. 2023. Application of small-sample machine learning method in evaluation of landslide disaster susceptibility in Xinjiang[J]. Journal of Engineering Geology, 31(4):1394-1406. doi: 10.13544 /j.cnki.jeg.2023-0226
Citation: Liang Longfei. 2023. Application of small-sample machine learning method in evaluation of landslide disaster susceptibility in Xinjiang[J]. Journal of Engineering Geology, 31(4):1394-1406. doi: 10.13544 /j.cnki.jeg.2023-0226
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小样本机器学习方法在新疆滑坡灾害易发性评价中的应用

doi: 10.13544/j.cnki.jeg.2023-0226

  • 中国矿业大学，徐州 221000，中国

基金项目: 

新疆维吾尔自治区重点研发计划项目 2021B03004-3

详细信息
    作者简介:

    梁龙飞(1997-)，男，硕士生，主要从事地质灾害与工程相互影响方面的研究. E-mail: TS22010127P31@cumt.edu.cn

  • 中图分类号: P642.22

APPLICATION OF SMALL-SAMPLE MACHINE LEARNING METHOD IN EVALUATION OF LANDSLIDE DISASTER SUSCEPTIBILITY IN XINJIANG

  • China University of Mining and Technology, Xuzhou 221000, China

Funds: 

Key R&D Program of Xinjiang Uygur Autonomous Region 2021B03004-3

    摘要
  • 摘要: 机器学习已经在滑坡易发性评价中大量应用且取得了较好的表现，但在进行大区域评价时，仍存在数据库样本需求量大，算力要求高；影响因素分级机械化，未考虑其与滑坡机理的相关性等情况。为减少数据库样本需求，本文提出了构建包含3种坡体状态的滑坡的数据库：已经发生过失稳的坡体、正处于失稳状态的坡体、失稳概率小的坡体，该数据库可以在数值上划分出临界值，便于模型更准确地识别滑坡，较大幅度地减小了数据量。针对影响因素分级机械化的问题，提出了基于频数分布图、累计曲线及其导数图的数理统计方式，更精细地描述因数与滑坡易发性的关系。以新疆滑坡灾害为例，验证了“包含3种坡体状态的数据库”与“基于数理统计的描述方法”的适用性，获得了新疆滑坡灾害易发性分区图。结果表明与传统数据库对比，在不明显改变精度的前提下，减少了90%以上的样本量；基于数理统计的描述方法可以绘制出更加细致的滑坡危险性分区图；活动性断裂和地形起伏度对新疆滑坡易发性起到重要的控制作用。
    Abstract: Machine learning has been widely used in the evaluation of landslide susceptibility,and has achieved good performance. However,there are still many problems in the evaluation of large areas. The problems include a large number of database samples and high computing power. When the impact factors are classified,their correlation with the landslide mechanism is not considered. In order to reduce the demand for database samples,this paper proposes to construct a slope state that includes three states:a slope that has already experienced instability,a slope that is in an unstable state,and a slope with a low probability of instability,and a database of landslides. The critical value is divided numerically to highlight the landslide,which is convenient for the model to identify the landslide more accurately and greatly reduces the amount of data. Aiming at the problem of grading mechanization of influencing factors,a mathematical statistical method based on frequency distribution diagram,cumulative curve and its derivative diagram is proposed to describe the relationship between factors and landslide susceptibility in a more precise manner. Taking the landslide disaster in Xinjiang as an example,the applicability of the "database containing three slope states" and the "description method based on mathematical statistics" is verified. A susceptibility zoning map of Xinjiang is obtained. Under the premise of remaining the accuracy,the sample size is reduced by 90%. The description method based on mathematical statistics can draw a more detailed landslide risk zoning map. Landslide susceptibility in Xinjiang is mainly controlled by active faults and land surface fluctuations.
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  • 图  1  新疆数字高程模型图

    Figure  1.  Digital elevation model map of Xinjiang

    下载: 全尺寸图片 幻灯片

    图  2  滑坡与距断裂距离关系图

    Figure  2.  Relationship between landslide and distance to fault

    下载: 全尺寸图片 幻灯片

    图  11  影响因素分级图

    Figure  11.  Hierarchical diagram of influencing factors

    下载: 全尺寸图片 幻灯片

    图  3  滑坡与距河流距离关系图

    Figure  3.  Relationship between landslide and distance to river

    下载: 全尺寸图片 幻灯片

    图  4  滑坡与水流强度指数关系图

    Figure  4.  The relationship between landslide and SPI

    下载: 全尺寸图片 幻灯片

    图  5  滑坡与地形湿度指数关系图

    Figure  5.  Relationship between landslide and TWI

    下载: 全尺寸图片 幻灯片

    图  6  滑坡与高程关系图

    Figure  6.  Relationship between landslide and DEM

    下载: 全尺寸图片 幻灯片

    图  7  滑坡与起伏度关系图

    Figure  7.  Relationship between landslide and TR

    下载: 全尺寸图片 幻灯片

    图  8  滑坡与坡度关系图

    Figure  8.  Relationship between landslide and slope angle

    下载: 全尺寸图片 幻灯片

    图  9  滑坡与坡向关系图

    Figure  9.  Relationship between landslide and slope aspect

    下载: 全尺寸图片 幻灯片

    图  10  滑坡与剖面曲率关系图

    Figure  10.  Relationship between landslide and PC

    下载: 全尺寸图片 幻灯片

    图  12  模型指标对比图

    Figure  12.  Comparison chart of model indicators

    下载: 全尺寸图片 幻灯片

    图  13  特征值重要性图

    Figure  13.  Eigenvalue importance map

    下载: 全尺寸图片 幻灯片

    图  14  滑坡易发性分区图

    Figure  14.  Landslide susceptibility map

    下载: 全尺寸图片 幻灯片

    表  1  基础数据来源表

    Table  1.   Basic data source table

    影响因素 数据来源
    数字高程 SRTM 30m
    坡度 SRTM 30m
    坡向 SRTM 30m
    剖面曲率 SRTM 30m
    水流强度指数 SRTM 30m
    起伏度 SRTM 30m
    距河流距离 新疆水文地质图
    地形湿度指数 SRTM 30m
    距断裂距离 中国及毗邻海区活动断裂分布图
    下载: 导出CSV

    表  2  支持向量机的超参数

    Table  2.   Hyperparameters of SVM

    种类 参数
    C 1
    kernel linear
    gamma 5
    下载: 导出CSV

    表  3  随机森林的超参数

    Table  3.   Hyperparameters of RF

    种类 参数
    n estimators 100
    max depth 5
    criterion Gini
    下载: 导出CSV

    表  4  多层感知机的超参数

    Table  4.   Hyperparameters of MLP

    种类 参数
    hidden layer sizes (10，10)
    activation Relu
    solver Adam
    max iter 1000
    下载: 导出CSV

    表  5  数据库对比表

    Table  5.   Database comparison table

    实验 张阳(2021) 孙德亮(2019) 周晓亭(2022) 本文
    研究区(×104km2) 0.2291 0.4087 0.2248 166
    滑坡 1251 1520 221 1204
    比值 0.546 0.372 0.090 0.001
    边坡状态 滑坡/非滑坡 滑坡/非滑坡 滑坡/非滑坡 滑坡/滑坡隐患点/非滑坡
    模型精度 89.94%~91.23% 83.00%~94.00% 50.55%~79.67% 57.00%~71.00%
    下载: 导出CSV
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  • 收稿日期:  2023-05-30
  • 修回日期:  2023-07-18
  • 刊出日期:  2023-08-25

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