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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
  • [1] 胡厚田. 崩塌与落石[M]. 北京: 中国铁道出版社, 1989: 1-2.

    HU H T. Collapse and rockfall[M]. Beijing: China Railway Publishing House, 1989: 1-2. (in Chinese)
    [2] 国土资源部地质环境司, 国土资源部宣传教育中心. 中国地质灾害与防治[M]. 北京: 地质出版社, 2003: 184-185.

    Geological Environment Department of the Ministry of Natural Resources of the People's Republic of China, Publicity and Education Center of the Ministry of NaturalResources of the People's Republic of China. Geological hazards and prevention in China[M]. Beijing: Geological Publishing House, 2003: 184-185. (in Chinese)
    [3] 曹洪洋, 袁颖, 贾磊. 区域降雨型滑坡灾害预警预报[M]. 北京: 地质出版社, 2017: 8-10.

    CAO H Y, YUAN Y, JIA L. Early warning and prediction of regional rainfall induced landslide[M]. Beijing: Geological Publishing House, 2017: 8-10. (in Chinese)
    [4] 温亚楠, 张志华, 慕号伟, 等. 动态多源数据驱动模式下的滑坡灾害空间预测[J]. 自然灾害学报, 2021, 30(3): 83-92. https://www.cnki.com.cn/Article/CJFDTOTAL-ZRZH202103010.htm

    WEN Y N, ZHANG Z H, MU H W, et al. Landslide disaster spatial prediction under dynamic multi-source data-driven mode[J]. Journal of Natural Disasters, 2021, 30(3): 83-92. (in Chinese with English abstract) https://www.cnki.com.cn/Article/CJFDTOTAL-ZRZH202103010.htm
    [5] ADA M, SAN B T. Comparison of machine-learning techniques for landslide susceptibility mapping using two-level randomsampling (2LRS) in Alakir catchment area, Antalya, Turkey[J]. Natural Hazards, 2018, 90(1): 237-263. doi: 10.1007/s11069-017-3043-8
    [6] 张书豪, 吴光. 随机森林与GIS的泥石流易发性及可靠性[J]. 地球科学, 2019, 44(9): 3115-3134. https://www.cnki.com.cn/Article/CJFDTOTAL-DQKX201909025.htm

    ZHANG S H, WU G. Debris flow susceptibility and its reliability based on random forest and GIS[J]. Earth Science, 2019, 44(9): 3115-3134. (in Chinese with English abstract) https://www.cnki.com.cn/Article/CJFDTOTAL-DQKX201909025.htm
    [7] 田乃满, 兰恒星, 伍宇明, 等. 人工神经网络和决策树模型在滑坡易发性分析中的性能对比[J]. 地球信息科学学报, 2020, 22(12): 2304-2316. https://www.cnki.com.cn/Article/CJFDTOTAL-DQXX202012004.htm

    TIAN N M, LAN H X, WU Y M, et al. Performance comparison of BP artificial neural network and CART decision tree model in landslide susceptibility prediction[J]. Journal of Geo-information Science, 2020, 22(12): 2304-2316. (in Chinese with English abstract) https://www.cnki.com.cn/Article/CJFDTOTAL-DQXX202012004.htm
    [8] 郭子正, 殷坤龙, 黄发明, 等. 基于滑坡分类和加权频率比模型的滑坡易发性评价[J]. 岩石力学与工程学报, 2019, 38(2): 287-300. https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX201902007.htm

    GUO Z Z, YIN K L, HUANG F M, et al. Evaluation of landslide susceptibility based on landslide classification and weighted frequency ratio model[J]. Chinese Journal of Rock Mechanics and Engineering, 2019, 38(2): 287-300. (in Chinese with English abstract) https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX201902007.htm
    [9] 武雪玲, 沈少青, 牛瑞卿. GIS支持下应用PSO-SVM模型预测滑坡易发性[J]. 武汉大学学报(信息科学版), 2016, 41(5): 665-671. https://www.cnki.com.cn/Article/CJFDTOTAL-WHCH201605015.htm

    WU X L, SHEN S Q, NIU R Q. Landslide susceptibility prediction using GIS and PSO-SVM[J]. Geomatics and Information Science of Wuhan University, 2016, 41(5): 665-671. (in Chinese with English abstract) https://www.cnki.com.cn/Article/CJFDTOTAL-WHCH201605015.htm
    [10] 黄发明, 殷坤龙, 蒋水华, 等. 基于聚类分析和支持向量机的滑坡易发性评价[J]. 岩石力学与工程学报, 2018, 37(1): 156-167. https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX201801016.htm

    HUANG F M, YIN K L, JIANG S H, et al. Landslide susceptibility assessment based on clustering analysis and support vector machine[J]. Chinese Journal of Rock Mechanics and Engineering, 2018, 37(1): 156-167. (in Chinese with English abstract) https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX201801016.htm
    [11] 余凯, 贾磊, 陈雨强, 等. 深度学习的昨天、今天和明天[J]. 计算机研究与发展, 2013, 50(9): 1799-1804. https://www.cnki.com.cn/Article/CJFDTOTAL-JFYZ201309002.htm

    YU K, JIA L, CHEN Y Q, et al. Deep learning: Yesterday, today and tomorrow[J]. Journal of Computer Research and Development, 2013, 50(9): 1799-1804. (in Chinese with English abstract) https://www.cnki.com.cn/Article/CJFDTOTAL-JFYZ201309002.htm
    [12] WANG H, ZHANG L, YIN K, et al. Landslide identification using machine learning[J]. Geoscience Frontiers, 2020, 12(1): 351-364.
    [13] SAHA S, ROY J, HEMBRAM T K, et al. Comparison between deep learning and tree-based machine learning approaches for landslide susceptibility mapping[J]. Water, 2021, 13(19): 2664-2693. doi: 10.3390/w13192664
    [14] 王毅, 方志策, 牛瑞卿, 等. 基于深度学习的滑坡灾害易发性分析[J]. 地球信息科学学报, 2021, 23(12): 2244-2260. https://www.cnki.com.cn/Article/CJFDTOTAL-DQXX202112012.htm

    WANG Y, FANG Z C, NIU R Q, et al. Landslide susceptibility analysis based on deep learning[J]. Journal of Geo-information Science, 2021, 23(12): 2244-2260. (in Chinese with English abstract) https://www.cnki.com.cn/Article/CJFDTOTAL-DQXX202112012.htm
    [15] GONG C, LIU T, YANG J, et al. Large-margin label-calibrated support vector machines for positive and unlabeled learning[J]. IEEE Trans. Neural Netw. Learn Syst., 2019, 30(11): 3471-3483. doi: 10.1109/TNNLS.2019.2892403
    [16] YAO J, QIN S, QIAO S, et al. Application of a two-step sampling strategy based on deep neural network for landslide susceptibility mapping[J]. Bulletin of Engineering Geology and the Environment, 2022, 81(4): 1-20.
    [17] HAN D, LI S, WEI F, et al. Two birds with one stone: Classifying positive and unlabeled examples on uncertain data streams[J]. Neurocomputing, 2018, 277: 149-160. doi: 10.1016/j.neucom.2017.03.094
    [18] VILLATORO-TELLO E, ANGUIANO E, MONTESY-GÓMEZ M, et al. Enhancing semi-supervised text classification using document summaries[C]//Anon. Ibero-American Conference on Artificial Intelligence. Switzerland: Cham, 2016: 115.
    [19] 戴悦. 基于信息量模型的三峡库区滑坡区域危险性评价方法研究[D]. 北京: 清华大学, 2013.

    DAI Y. Study on the method of regional early warning of landslide in Three Gorges area based on information model[D]. Beijing: Tsinghua University, 2013. (in Chinese with English abstract)
    [20] 周晓亭, 黄发明, 吴伟成, 等. 基于耦合信息量法选择负样本的区域滑坡易发性预测[J]. 工程科学与技术, 2022, 54(3): 25-35.

    ZHOU X T, HUANG F M, WU W C, et al. Regional landslide susceptibility prediction based on negative sample selected by coupling information value method[J]. Advanced Engineering Science, 2022, 54(3): 25-35. (in Chinese with English abstract)
    [21] 陈飞, 蔡超, 李小双, 等. 基于信息量与神经网络模型的滑坡易发性评价[J]. 岩石力学与工程学报, 2020, 39(增刊1): 2859-2870. https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX2020S1027.htm

    CHEN F, CAI C, LI X S, et al. Evaluation of landslide susceptibility based on information volume and neural network model[J]. Chinese Journal of Rock Mechanics and Engineering, 2020, 39(S1): 2859-2870. (in Chinese with English abstract) https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX2020S1027.htm
    [22] 温鑫, 范宣梅, 陈兰, 等. 基于信息量模型的地质灾害易发性评价: 以川东南古蔺县为例[J]. 地质科技通报, 2022, 41(2): 290-299. doi: 10.19509/j.cnki.dzkq.2022.0054

    WEN X, FAN X M, CHEN L, et al. Susceptibility assessment of geological disasters based on an information value model: A case of Gulin County in southeast Sichuan[J]. Bulletin of Geological Science and Technology, 2022, 41(2): 290-299. (in Chinese with English abstract) doi: 10.19509/j.cnki.dzkq.2022.0054
    [23] 殷坤龙. 滑坡灾害预测预报[M]. 武汉: 中国地质大学出版, 2001: 19-22.

    YIN K L. Time prediction and risk evaluation of landslide hazard and prospective[M]. Wuhan: China University of Geosciences Press, 2001: 19-22. (in Chinese)
    [24] LECUN Y, BOSER B, DENKER J S, et al. Backpropagation applied to handwritten zip code recognition[J]. Neural Computation, 1989, 1(4): 541-551. doi: 10.1162/neco.1989.1.4.541
    [25] GU J, WANG Z, KUEN J, et al. Recent advances in convolutional neural networks[J]. Pattern Recognition, 2018, 77: 354-377. doi: 10.1016/j.patcog.2017.10.013
    [26] 丁世飞, 齐丙娟, 谭红艳. 支持向量机理论与算法研究综述[J]. 电子科技大学学报, 2011, 40(1): 2-10. https://www.cnki.com.cn/Article/CJFDTOTAL-DKDX201101003.htm

    DING S F, QI B J, TAN H Y. An overview on theory and algorithm of support vector machines[J]. Journal of University of Electronic Science and Technology of China, 2011, 40(1): 2-10. (in Chinese with English abstract) https://www.cnki.com.cn/Article/CJFDTOTAL-DKDX201101003.htm
    [27] 周志华. 机器学习[M]. 北京: 清华大学出版社, 2016: 178-181.

    ZHOU Z H. Machine learning[M]. Beijing: Tsinghua University Publishing House, 2016: 178-181. (in Chinese)
    [28] 黄发明, 胡松雁, 闫学涯, 等. 基于机器学习的滑坡易发性预测建模及其主控因子识别[J]. 地质科技通报, 2022, 41(2): 79-90. doi: 10.19509/j.cnki.dzkq.2021.0087

    HUANG F M, HU S Y, YAN X Y, et al. Landslide susceptibility prediction and its main environmental factors identification based on machine learning models[J]. Bulletin of Geological Science and Technology, 2022, 41(2): 79-90. (in Chinese with English abstract) doi: 10.19509/j.cnki.dzkq.2021.0087
    [29] VU D H, MUTTAQI K M, AGALGAONKAR A P. A variance inflation factor and backward elimination based robust regression model for forecasting monthly electricity demand using climatic variables[J]. Applied Energy, 2015, 140: 385-394. doi: 10.1016/j.apenergy.2014.12.011
    [30] 闫举生, 谭建民. 基于不同因子分级法的滑坡易发性评价: 以湖北远安县为例[J]. 中国地质灾害与防治学报, 2019, 30(1): 52-60. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDH201901006.htm

    YAN J S, TAN J M. Landslide susceptibility assessment based on different factor classification methods: A case study in Yuan'an County of Hubei Province[J]. The Chinese Journal of Geological Hazard and Control, 2019, 30(1): 52-60. (in Chinese with English abstract) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDH201901006.htm
    [31] 李婷婷, 吕佳, 范伟亚. 基于新型间谍技术的半监督自训练正例无标记学习[J]. 计算机应用, 2019, 39(10): 2822-2828. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJY201910006.htm

    LI T T, LÜ J, FAN W Y. Semi-supervised self-training positive and unlabeled learning based on new Spy technology[J]. Journal of Computer Application, 2019, 39(10): 2822-2828. (in Chinese with English abstract) https://www.cnki.com.cn/Article/CJFDTOTAL-JSJY201910006.htm
    [32] JUBA B, LE H S. Precision-recall versus accuracy and the role of large data sets[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2019, 33: 4039-4048. doi: 10.1609/aaai.v33i01.33014039
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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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