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基于机器学习的区域滑坡危险性评价方法综述

方然可 刘艳辉 黄志全

方然可, 刘艳辉, 黄志全. 基于机器学习的区域滑坡危险性评价方法综述[J]. 中国地质灾害与防治学报, 2021, 32(4): 1-8. doi: 10.16031/j.cnki.issn.1003-8035.2021.04-01
引用本文: 方然可, 刘艳辉, 黄志全. 基于机器学习的区域滑坡危险性评价方法综述[J]. 中国地质灾害与防治学报, 2021, 32(4): 1-8. doi: 10.16031/j.cnki.issn.1003-8035.2021.04-01
Ranke FANG, Yanhui LIU, Zhiquan HUANG. A review of the methods of regional landslide hazard assessment based on machine learning[J]. The Chinese Journal of Geological Hazard and Control, 2021, 32(4): 1-8. doi: 10.16031/j.cnki.issn.1003-8035.2021.04-01
Citation: Ranke FANG, Yanhui LIU, Zhiquan HUANG. A review of the methods of regional landslide hazard assessment based on machine learning[J]. The Chinese Journal of Geological Hazard and Control, 2021, 32(4): 1-8. doi: 10.16031/j.cnki.issn.1003-8035.2021.04-01

基于机器学习的区域滑坡危险性评价方法综述

doi: 10.16031/j.cnki.issn.1003-8035.2021.04-01
基金项目: 国家重点研发计划(2018YFC1505503);国家科技支撑计划子课题(2015BAK10B021);国家自然科学基金项目(41202217);中原科技创新领军人才计划资助项目(214200510030)
详细信息
    作者简介:

    方然可(1996-),男,河南郑州人,硕士研究生,主要从事滑坡灾害预警相关研究工作。E-mail:1361853780@qq.com

    通讯作者:

    刘艳辉(1978-),女,博士,教授级高级工程师,主要从事滑坡灾害预警与防治、工程地质等方面的研究工作。E-mail:392990563@qq.com

  • 中图分类号: P642.22

A review of the methods of regional landslide hazard assessment based on machine learning

  • 摘要: 我国滑坡灾害分布范围广,危害严重。区域滑坡危险性评价一直都是滑坡灾害防灾减灾的重要内容之一。近年来,随着大数据和人工智能技术的飞速发展,机器学习技术逐渐在滑坡灾害危险性评价方面得到广泛应用,并取得了较好效果。在大量研读文献的基础上,系统阐述了基于机器学习技术的滑坡危险性评价方法研究现状。综述从评价因子选择与量化归一化、数据清洗与样本集构建、模型选取与训练评价等三个关键环节对现有研究成果进行分析评述,最后对机器学习滑坡危险性评价方法的发展趋势提出讨论意见。
  • 表  1  滑坡危险性评价可选用的机器学习模型

    Table  1.   An optional machine learning model for landslide hazard assessment

    类型常用模型优缺点相关数学公式
    分类
    (判断类别已知的
    离散型数据)
    KNN最近邻算法适用多分类评价;准确度高,对异常点
    不敏感。但计算量大,过于依赖均衡训
    练数据。
    欧式距离:$d(x,y) = \sqrt {\displaystyle\sum\limits_{k = 1}^n { { {\left( { {x_k} - {y_k} } \right)}^2} } }$
    曼哈顿距离:$d(x,y) = \sqrt {\displaystyle\sum\limits_{k = 1}^n {\left| { {x_k} - {y_k} } \right|} }$
    SVM支持向量机核函数可映射至高维空间,解决非线性
    分类评价。但对大规模和多分类训练样
    本难以进行评价。
    高斯核函数:$K({\rm{X} },{\rm{Y} }) = \exp \left\{ { - \dfrac{ {||X - Y||{^2} } }{ {2{\sigma ^2} } } } \right\}$
    人工神经网络
    (线性、BP、卷积)
    可高速寻找优化解。但需要大量参数,
    学习时间过长,评价结果不确定。
    损失函数$L = \dfrac{1}{2}\displaystyle\sum\limits_{i = 1}^{ {m_K} } { { {\left( {Y_i^{(K)} - {T_i} } \right)}^2} } = \dfrac{1}{2}\displaystyle\sum\limits_{i = 1}^{ {m_K} } { { {\left( { {\delta _i} } \right)}^2} }$
    Logistic回归
    (Sigmoid函数、梯度上升)
    评价效率高。但不能观察学习过程。逻辑函数:$y = \dfrac{1}{ {1 + {e^{ - x} } } }$
    决策树适合评价离散小规模样本。但评价大量
    连续变量和多类别样本效果欠佳。
    信息熵${H(X) = - \displaystyle\sum\limits_{x\varepsilon X} P (x){\rm{log}}_2 P(x)}$
    集成算法
    (bagging、随机森林RF、
    boosting、stacking)
    避免了强势样本对评价结果的影响。但
    在某些噪音值较大的样本来进行危险性
    评价时可能会发生过拟合现象。
    Bagging $f(x) = 1/M\displaystyle\sum\limits_{m = 1}^M { {f_m} } (x)$
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-09-04
  • 修回日期:  2020-09-14
  • 网络出版日期:  2021-08-19
  • 刊出日期:  2021-08-25

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