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不同栅格尺寸下输电线路地质灾害易发性评价

邬礼扬 殷坤龙 曾韬睿 刘书豪 刘真意

邬礼扬, 殷坤龙, 曾韬睿, 刘书豪, 刘真意. 不同栅格尺寸下输电线路地质灾害易发性评价[J]. 地质科技通报, 2024, 43(1): 241-252. doi: 10.19509/j.cnki.dzkq.tb20220307
引用本文: 邬礼扬, 殷坤龙, 曾韬睿, 刘书豪, 刘真意. 不同栅格尺寸下输电线路地质灾害易发性评价[J]. 地质科技通报, 2024, 43(1): 241-252. doi: 10.19509/j.cnki.dzkq.tb20220307
WU Liyang, YIN Kunlong, ZENG Taorui, LIU Shuhao, LIU Zhenyi. Evaluation of geological disaster susceptibility of transmission lines under different grid resolutions[J]. Bulletin of Geological Science and Technology, 2024, 43(1): 241-252. doi: 10.19509/j.cnki.dzkq.tb20220307
Citation: WU Liyang, YIN Kunlong, ZENG Taorui, LIU Shuhao, LIU Zhenyi. Evaluation of geological disaster susceptibility of transmission lines under different grid resolutions[J]. Bulletin of Geological Science and Technology, 2024, 43(1): 241-252. doi: 10.19509/j.cnki.dzkq.tb20220307

不同栅格尺寸下输电线路地质灾害易发性评价

doi: 10.19509/j.cnki.dzkq.tb20220307
基金项目: 

国家重点研发计划项目 2018YFC0809402

详细信息
    作者简介:

    邬礼扬, E-mail: wuliyang@cug.edu.cn

    通讯作者:

    殷坤龙, E-mail: yinkl@126.com

  • 中图分类号: P642;P694

Evaluation of geological disaster susceptibility of transmission lines under different grid resolutions

More Information
  • 摘要:

    输电线路的安全运行对国家经济建设与发展具有重要意义,而针对输电线路进行地质灾害易发性评价的研究较少。以京津冀地区的输电线路为例,选取高程、坡度、坡向、地形起伏度、地层岩性、距断层距离、距水系距离、土地利用类型8个指标因子,采用频率比法对各指标因子进行分级,构建易发性评价体系。再利用不同的机器学习模型,使用不同尺寸的栅格单元作为评价单元对研究区进行易发性评价。最后,选取精度最高的机器学习模型与传统的层次分析法完成研究区易发性区划图。研究结果表明:贝叶斯网络模型在区域输电线路易发性评价中的应用效果最好,模型性能最强,最高AUC值为0.876。与传统的层次分析法相比,BN模型在研究区易发性制图中的效果更好,精度更高。此外,采用50 m的栅格作为评价单元在研究区易发性评价中取得了最好的应用效果,研究成果为输电线路地质灾害易发性评价以及栅格尺寸的选用提供了思路以及参考。

     

  • 图 1  输电线路地质灾害易发性评价流程图

    Figure 1.  Flow chart of geological disaster susceptibility evaluation of transmission lines

    图 2  研究区输电线路位置(a)和地质灾害分布(b)

    Figure 2.  Location of transmission lines(a) and distribution of geological hazards(b) in the study area

    图 3  指标因子分区图

    a.高程;b.坡度;c.坡向;d.地形起伏度;e.岩性;f.距断层距离;g.距水系距离;h.土地利用类型

    Figure 3.  Index factor partition map

    图 4  不同栅格尺寸机器学习模型测试集AUC

    Figure 4.  AUC values of the machine learning model test set under different grid sizes

    图 5  地质灾害易发性区划图

    a.BN模型; b.层次分析法模型

    Figure 5.  Geological disaster susceptibility zoning map

    表  1  判断矩阵标度及含义

    Table  1.   Meaning and scale of the judgment matrix

    标度值 含义
    1 2个因素相比,具有同等重要性
    3 2个因素相比,前者比后者稍重要
    5 2个因素相比,前者比后者明显重要
    7 2个因素相比,前者比后者强烈重要
    9 2个因素相比,前者比后者极端重要
    2, 4, 6, 8 表示上述相邻判断的中间值
    1/i(i=1, 2, …, 9) 与上述情况相反
    下载: 导出CSV

    表  2  指标因子多重共线性分析

    Table  2.   Multicollinearity analysis of index factors

    评价因子 方差膨胀因子 容忍度
    高程 1.255 0.797
    坡度 4.273 0.234
    坡向 1.002 0.998
    地形起伏度 4.280 0.234
    地层岩性 1.053 0.950
    距断层距离 1.179 0.848
    距水系距离 1.028 0.972
    土地利用类型 1.021 0.980
    下载: 导出CSV

    表  3  不同模型的评估指标

    Table  3.   Evaluation indicators of different models

    模型 阶段 AUC ACC precision TPR TNR MCC RMSE MAE
    RBF 训练 0.846 0.779 0.743 0.850 0.709 0.565 0.158 0.319
    测试 0.844 0.775 0.742 0.848 0.701 0.557 0.157 0.319
    BN 训练 0.877 0.816 0.767 0.906 0.726 0.644 0.132 0.242
    测试 0.876 0.813 0.768 0.902 0.723 0.637 0.133 0.243
    SVM 训练 0.863 0.797 0.763 0.852 0.744 0.599 0.150 0.301
    测试 0.857 0.797 0.774 0.861 0.728 0.595 0.152 0.303
    MLP 训练 0.871 0.815 0.779 0.876 0.754 0.635 0.134 0.269
    测试 0.871 0.810 0.777 0.873 0.746 0.626 0.136 0.272
    LR 训练 0.773 0.738 0.714 0.781 0.695 0.479 0.191 0.385
    测试 0.819 0.787 0.787 0.805 0.768 0.575 0.175 0.372
    注:AUC:预测为正的概率值比预测为负的概率值还要大的可能性;总体分类精度(accuracy, 简称ACC):预测正确样本数/所有样本数;精确度(precision): 正确预测正分类数/预测为正样本分类数;TPR:预测为正类的样本数/所有正样本数;TNR:预测为反类的样本/反类样本总数;MCC(matthews correlation coefficient): 分类与预测分类之间的相关系数;均方根误差(RMSE)以及平均绝对误差(MAE)
    下载: 导出CSV

    表  4  栅格统计

    Table  4.   Raster statistics

    易发性等级 滑坡栅格数 各等级栅格数 占总栅格比例/% 占总灾害比例/% 灾害比率
    层次分析法 BN 层次分析法 BN 层次分析法 BN 层次分析法 BN 层次分析法 BN
    144 375 8 962 333 15 219 038 41.7 70.7 0.9 2.4 0.022 0.034
    838 408 4 442 850 1 281 954 20.7 6.0 5.4 2.6 0.263 0.443
    4 680 7 960 4 160 518 3 644 382 19.3 16.9 30.3 51.5 1.566 3.041
    极高 9 792 6 711 3 947 443 1 380 308 18.3 6.4 63.4 43.4 3.453 6.768
    下载: 导出CSV
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  • 收稿日期:  2022-06-23
  • 录用日期:  2022-09-28
  • 修回日期:  2022-09-23

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