Effect of sample selection on the susceptibility assessment of geological hazards: A case study in Liulin County, Shanxi Province
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摘要: 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（RF）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和1∶10和非地质灾害点距已知灾害点100 m、500 m、800 m和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(MAE)和均方根误差(RMSE)整体下降，准确率(ACC)整体上升。各模型AUC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RF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。Abstract: The rational selection of non-geological hazard samples is of great significance to improve the accuracy of geological hazard susceptibility prediction. This study uses Liulin County as a case study, where appropriate impact factors were selected, and the random forest (RF) model was employed for susceptibility assessment based on GIS technology. A total of twenty sets of models were created by varying the ratio of geological hazard to non-geological hazard points (1∶1, 1∶1.5, 1∶3, 1∶5 and 1∶10) and the distance from non-geological hazard points to known hazard points (100 m, 500 m, 800 m and 1000 m). The results demonstrate that: (1) Through error index, confusion matrix, and ROC curve tests, the sample proportion and distance from the known hazard point significantly influenced the geological hazard susceptibility evaluation. As the sample proportion decreased and the distance from known hazard points increased, the overall MAE and RMSE of the models decreased, while the overall ACC increased. All models achieved AUC value greater than 0.8, indicating excellent predictive performance. When the sample proportion was less than 1∶3, the increasing distance from the known hazard points on model error and accuracy became less pronounced, stabilizing the results. The most suitable model for the study area was found to have a sample ratio of 1∶10 and a distance of 1000 m from known hazard points. (2) High and very high susceptibility areas were primarily located in the central and northern regions, adjacent to roads and rivers, making them key areas for hazard prevention and reduction in Liulin County. (3) Differences in sample selection led to varying susceptibility results mainly due to changes in the RF model's data feature collection and judgment during the modeling process, as well as the representativeness of the samples. These research findings hold significant implications for the implementation of hazard prevention and reduction measures.
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Key words:
- non-geological hazard /
- GIS /
- random forest /
- susceptibility /
- error /
- confusion matrix /
- ROC
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表 1 MAE和RMSE值
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 AME 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 AME 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 AME 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 AME 0.281 0.254 0.267 0.188 0.129 RMSE 0.368 0.337 0.388 0.302 0.258 
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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
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表 3 ACC
Table 3. Summary table of ACC values
距离 1∶1 1∶1.5 1∶3 1∶5 1∶10 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
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