中国地质环境监测院
中国地质灾害防治工程行业协会
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样本选取对地质灾害易发性评价的影响

陈建平, 辛亚波, 王泽鹏, 陈伟, 万长园, 刘云艳, 黄俊杰. 样本选取对地质灾害易发性评价的影响——以山西柳林县为例[J]. 中国地质灾害与防治学报, 2024, 35(3): 152-162. doi: 10.16031/j.cnki.issn.1003-8035.202210037
引用本文: 陈建平, 辛亚波, 王泽鹏, 陈伟, 万长园, 刘云艳, 黄俊杰. 样本选取对地质灾害易发性评价的影响——以山西柳林县为例[J]. 中国地质灾害与防治学报, 2024, 35(3): 152-162. doi: 10.16031/j.cnki.issn.1003-8035.202210037
CHEN Jianping, XIN Yabo, WANG Zepeng, CHEN Wei, WAN Changyuan, LIU Yunyan, HUANG Junjie. Effect of sample selection on the susceptibility assessment of geological hazards: A case study in Liulin County, Shanxi Province[J]. The Chinese Journal of Geological Hazard and Control, 2024, 35(3): 152-162. doi: 10.16031/j.cnki.issn.1003-8035.202210037
Citation: CHEN Jianping, XIN Yabo, WANG Zepeng, CHEN Wei, WAN Changyuan, LIU Yunyan, HUANG Junjie. Effect of sample selection on the susceptibility assessment of geological hazards: A case study in Liulin County, Shanxi Province[J]. The Chinese Journal of Geological Hazard and Control, 2024, 35(3): 152-162. doi: 10.16031/j.cnki.issn.1003-8035.202210037

样本选取对地质灾害易发性评价的影响

  • 基金项目: 国家自然科学基金项目(51604140)
详细信息
    作者简介: 陈建平(1971—),男,山西保德人,地质资源与地质工程专业,博士,副教授,主要从事工程地质、水文地质等方向研究。E-mail:13804181164@139.com
    通讯作者: 王泽鹏(1997—),男,山西长治人,资源与环境专业,硕士研究生,主要从事地质工程和地理信息系统方向研究。E-mail:449139098@qq.com
  • 中图分类号: P642.22

Effect of sample selection on the susceptibility assessment of geological hazards: A case study in Liulin County, Shanxi Province

More Information
  • 非地质灾害样本的合理选取对地质灾害易发性预测准确度的提高具有重要意义。文章以柳林县为例,选取适宜的影响因子,基于GIS技术采用随机森林模型进行易发性评价。以地质灾害与非地质灾害比例为1∶1、1∶1.5、1∶3、1∶5、1∶10和非地质灾害点距已知灾害点100,500,800,1000 m为选取条件交叉结合共创建20组模型进行分析。结果表明:(1)通过误差指标、混淆矩阵和ROC曲线检验,样本比例和距已知灾害点距离变化对地质灾害易发性评价结果有较大影响。随着样本比例变小,距已知灾害点距离增加,各模型平均绝对误差和均方根误差整体下降,准确率整体上升。各模型ROC曲线下面积值均大于0.8,均有较好的预测效果。当样本比例小于1∶3时,距已知灾害点距离增加对模型误差和准确率影响较小,变化趋于稳定。综合判断样本比例为1∶10、距已知灾害点1000 m为最适合研究区模型。(2)高和极高易发区主要分布在中部及北部道路和河流两侧的地区,是柳林县防灾减灾的重点区。(3)样本选取差异导致易发性结果不同主要是因为建模过程中随机森林模型对数据特征的采集及判断发生变化,样本是否具有代表性发生变化。这些研究成果对当防灾减灾工作的实施具有重要意义。

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  • 图 1  研究区位置

    Figure 1. 

    图 2  因子分级图

    Figure 2. 

    图 3  易发性分区图

    Figure 3. 

    图 4  AUC值折线图

    Figure 4. 

    图 5  MAE值折线图和RMSE值折线图

    Figure 5. 

    图 6  ACC折线图

    Figure 6. 

    图 7  ROC曲线

    Figure 7. 

    表 1  MAERMSE

    Table 1.  MAE and RMSE values

    距离 误差统计指标 1∶1 1∶1.5 1∶3 1∶5 1∶10
    计算数据 距已知灾害点100 m MAE 0.279 0.285 0.275 0.196 0.136
    RMSE 0.373 0.367 0.408 0.315 0.267
    距已知灾害点500 m MAE 0.332 0.304 0.270 0.205 0.130
    RMSE 0.410 0.401 0.393 0.322 0.260
    距已知灾害点800 m MAE 0.323 0.279 0.264 0.181 0.135
    RMSE 0.414 0.361 0.385 0.280 0.269
    距已知灾害点1000 m MAE 0.281 0.254 0.267 0.188 0.129
    RMSE 0.368 0.337 0.388 0.302 0.258
    下载: 导出CSV

    表 2  混淆矩阵

    Table 2.  Summary table of confusion matrix

    地质灾害与非地质灾害样本比例1∶1
    距已知灾害点100 m 真实值/个 距已知灾害点500 m 真实值/个
    地质灾害 非地质灾害 地质灾害 非地质灾害
    预测值 地质灾害 34 7 预测值 地质灾害 26 15
    非地质灾害 11 39 非地质灾害 11 39
    距已知灾害点800 m 真实值/个 距已知灾害点1000 m 真实值/个
    地质灾害 非地质灾害 地质灾害 非地质灾害
    预测值 地质灾害 31 10 预测值 地质灾害 35 6
    非地质灾害 13 37 非地质灾害 10 40
    ......
    地质灾害与非地质灾害样本比例1∶10
    距已知灾害点800 m 真实值/个 距已知灾害点1000 m 真实值/个
    地质灾害 非地质灾害 地质灾害 非地质灾害
    预测值 地质灾害 435 13 预测值 地质灾害 442 6
    非地质灾害 38 13 非地质灾害 41 10
    下载: 导出CSV

    表 3  ACC值表

    Table 3.  Summary table of ACC values

    距离 1∶1
    占比/%
    1∶1.5
    占比/%
    1∶3
    占比/%
    1∶5
    占比/%
    1∶10
    占比/%
    距已知灾害点100 m 80.2 79.8 78.0 86.0 90.1
    距已知灾害点500 m 71.4 77.2 78.0 86.0 91.4
    距已知灾害点800 m 74.7 80.7 79.7 87.9 89.8
    距已知灾害点1000 m 82.4 82.5 80.8 89.3 92.3
    下载: 导出CSV
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出版历程
收稿日期:  2022-10-25
修回日期:  2023-02-05
录用日期:  2023-08-03
刊出日期:  2024-06-25

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