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基于改进两步法采样策略和卷积神经网络的崩塌易发性评价

邓日朗 张庆华 刘伟 陈凌伟 谭建辉 高泽茂 郑先昌

邓日朗, 张庆华, 刘伟, 陈凌伟, 谭建辉, 高泽茂, 郑先昌. 基于改进两步法采样策略和卷积神经网络的崩塌易发性评价[J]. 地质科技通报, 2024, 43(2): 186-200. doi: 10.19509/j.cnki.dzkq.tb20220535
引用本文: 邓日朗, 张庆华, 刘伟, 陈凌伟, 谭建辉, 高泽茂, 郑先昌. 基于改进两步法采样策略和卷积神经网络的崩塌易发性评价[J]. 地质科技通报, 2024, 43(2): 186-200. doi: 10.19509/j.cnki.dzkq.tb20220535
DENG Rilang, ZHANG Qinghua, LIU Wei, CHEN Lingwei, TAN Jianhui, GAO Zemao, ZHENG Xianchang. Collapse susceptibility evaluation based on an improved two-step sampling strategy and a convolutional neural network[J]. Bulletin of Geological Science and Technology, 2024, 43(2): 186-200. doi: 10.19509/j.cnki.dzkq.tb20220535
Citation: DENG Rilang, ZHANG Qinghua, LIU Wei, CHEN Lingwei, TAN Jianhui, GAO Zemao, ZHENG Xianchang. Collapse susceptibility evaluation based on an improved two-step sampling strategy and a convolutional neural network[J]. Bulletin of Geological Science and Technology, 2024, 43(2): 186-200. doi: 10.19509/j.cnki.dzkq.tb20220535

基于改进两步法采样策略和卷积神经网络的崩塌易发性评价

doi: 10.19509/j.cnki.dzkq.tb20220535
基金项目: 

广州市城市规划勘测设计研究院咨询项目 2023岩28008B-合01

详细信息
    作者简介:

    邓日朗, E-mail: rldeng@foxmail.com

    通讯作者:

    郑先昌, E-mail: zhengxianchang@gzhu.edu.cn

  • 中图分类号: P642.22

Collapse susceptibility evaluation based on an improved two-step sampling strategy and a convolutional neural network

More Information
  • 摘要:

    机器学习在崩塌滑坡泥石流地质灾害易发性分析评价领域已得到广泛的研究性应用, 非灾害样本的选取是易发性建模过程中的关键问题, 传统随机抽样和手工标注方法可能存在随机性和主观性。将土质崩塌易发性评价视为正例无标记(positive and unlabeled, 简称PU)学习, 提出了一种结合信息量(information value, 简称IV)和间谍技术(Spy)的两步卷积神经网络(convolutional neural networks, 简称CNN)框架(ISpy-CNN)。以广州市黄埔区崩塌编录和15类基础环境因子, 通过信息量模型筛选出部分低信息量样本; 采用间谍技术训练CNN模型, 从低信息量样本中识别出具有高置信度的可靠负例划分为非崩塌样本; 分别基于该学习框架、传统间谍技术和随机抽样, 使用支持向量机(support vector machine, 简称SVM)和随机森林(random forest, 简称RF)对比验证。结果表明, ISpy-CNN框架在验证集上的准确率、F1值、敏感度和特异度较随机采样分别提升了6.82%, 6.82%, 6.82%, 8.23%, 较传统Spy技术分别提升了2.86%, 2.89%, 2.86%, 2.31%;PU学习中第2步采用CNN模型的预测精度高于RF和SVM模型; 与传统Spy技术相比, 增加相同数量训练样本, ISpy-CNN框架筛选的样本集表现出较高的稳定性、预测精度和增长率。本研究提出的ISpy-CNN框架能更好地辅助选取高质量非灾害样本, 且崩塌易发性分区结果更符合实际的崩塌空间分布。

     

  • 图 1  PU学习分类过程(图中代号含义见正文)

    Figure 1.  Classification process of PU learning

    图 2  基于卷积神经网络和改进两步法采样策略的崩塌易发性预测流程图

    Figure 2.  Flowchart of collapse prediction based on a convolutional neural network and an improved two-step sampling strategy

    图 3  研究区崩塌灾害分布概况(a)以及鼓胀式崩塌(b)、滑移式崩塌(c)和倾倒式崩塌(d)示意图

    Figure 3.  Distribution of collapse disasters in the study area (a) and rotational sliding failure (b), planar sliding failure (c), toppling failure(d)

    图 4  研究区崩塌易发性评价因子分级图(地层代号含义见正文)

    Figure 4.  Grading diagram of evaluation factors for collapse susceptibility in the study area

    图 5  影响因子相关性分析

    Figure 5.  Correlation analysis of impact factors

    图 6  信息量模型评价结果(a)和基于ISpy方法选取的非崩塌样本分布(b)示意图

    Figure 6.  Results obtained through information value model evaluation result (a) and distribution of noncollapse samples selection based on the ISpy method (b)

    图 7  研究区崩塌灾害易发性分级图

    Figure 7.  Grading map of collapse disaster susceptibility in the study area

    图 8  基于Spy(a)和ISpy(b)采样方法的崩塌易发性预测模型ROC曲线

    Figure 8.  ROC curves for model of collapse susceptibility prediction using the Spy (a) and ISpy (b) sampling methods

    图 9  基于Spy(a)和ISpy(b)采样方法的崩塌易发性指数分布(IQR为四分位间距)

    Figure 9.  Correlation between the index distribution and sampling methods using the Spy (a) and ISpy (b) sampling methods

    图 10  基于Spy(a)和ISpy(b)采样方法的预测准确率与训练集样本数据量的关系

    Figure 10.  Correlation between prediction accuracy and the number of training samples using the Spy (a) and ISpy (b) sampling methods

    表  1  CNN模型部分超参数设置

    Table  1.   Hyperparameter settings of the proposed CNN model

    CNN参数项 参数值
    卷积核大小 3×1
    池化核大小 2×1
    激活函数 ReLU
    优化器 Adam
    损失函数 二元交叉熵损失函数
    学习率 0.005
    下载: 导出CSV

    表  2  使用不同采样方法的CNN模型分类结果对比

    Table  2.   Performance comparison of CNN models using different sampling methods

    方法 ACC/% F1/% 敏感度/% 特异度/%
    随机采样 85.08 85.07 85.08 84.94
    Spy 89.04 89.00 89.04 90.86
    ISpy 91.91 91.90 91.91 93.17
    下载: 导出CSV

    表  3  崩塌易发性评价频率比统计结果

    Table  3.   Frequency ratio statistics of collapse susceptibility evaluation

    易发性等级 Spy ISpy
    SVM RF CNN SVM RF CNN
    极低 0.00 0.00 0.03 0.03 0.00 0.01
    0.32 0.22 0.48 0.47 0.10 0.16
    中等 1.54 0.83 1.12 1.23 0.57 0.44
    4.94 2.25 1.35 4.49 1.59 1.06
    极高 6.48 7.46 7.83 6.75 8.00 8.19
    下载: 导出CSV
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  • 收稿日期:  2022-09-19
  • 录用日期:  2022-12-07
  • 修回日期:  2022-12-02

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