Susceptibility analysis of geological hazards based on the random forest weighted information value model: A case study of Shidian County,Yunnan Province
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摘要:
传统的信息量模型在进行地质灾害易发性评价时，通常只是简单地将各个评价因子的信息量值累加，而忽略了因子之间权重的差异，这在一定程度上影响了易发性分区的科学性和合理性。为了克服这个问题，文章以云南省施甸县为例，引入了随机森林模型来计算各评价因子的权重。在构建了合适的评价指标体系后，逐一计算每个因子的信息量及其权重，然后进行加权求和。按照等间隔分级法，将研究区域划分为极高、高、中、低4个易发性等级。为了验证模型的准确性，选取了近3年内该区最新调查-重点区域地质灾害精细化调查与风险评价成果得到的地质灾害隐患点与易发性分区进行叠加，并通过隐患点密度和ROC曲线进行精度检验对比分析。对比研究结果发现，引入随机森林赋权后，极高易发内隐患点密度由1.754升至1.926，AUC值从0.809升至0.847。研究结果表明：在单一信息量模型中引入随机森林进行赋权能有效表达因子间的权重差异，提升地质灾害易发性分区的精度，在实际应用中具有更高的准确性。
Abstract:Traditional information value models for evaluating geological hazard susceptibility typically involve simply summing the information values of various evaluation factors, without considering the differences in weight among these factors. This can affect the scientific rigor and rationality of susceptibility zoning to some extent. To address this issue, this paper takes Shidian County of Yunnan Province as an example and introduces the random forest model to calculate the weights of each evaluation factor. After constructing an appropriate evaluation index system, the information value and weight of each factor are calculated individually, followed by a weighted summation. According to the equal interval classification method, the study area is then divided into four susceptibility levels--extremely high, high, medium, and low. To verify the accuracy of the model, the latest geological hazard hidden points identified through detailed investigations and risk assessments over the past 3 a were overlaid with the susceptibility zones. The accuracy was analyzed through hazard point density analysis and ROC curve comparison. Based on the comparison of research results, after introducing the random forest weighting, the density of extremely high-risk hidden hazard points increased from 1.754 to 1.926, and the AUC value improved from 0.809 to 0.847. The research results indicate that introducing random forest for weighting in a single information quantity model can effectively reflects the weight differences among factors, enhancing the precision of geological disaster susceptibility zoning. This method shows higher accuracy in practical applications.
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Key words:
- random forest /
- information model /
- geological hazard /
- susceptibility evaluation
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表 1 地质数据来源
Table 1. Sources of geological data
数据 数据来源及比例尺 数据格式 1 坡度 ASTER GDEM v3 30 m分辨率数字高程数据 GeoTIFF 2 地形起伏度 ASTER GDEM v3 30 m分辨率数字高程数据 GeoTIFF 3 工程岩组 云南省重点区域地质灾害精细化调查与风险评价（2022） MapGIS 4 断层 云南省重点区域地质灾害精细化调查与风险评价（2022） MapGIS 5 河网密度 ASTER GDEM v3 30 m分辨率数字高程数据 GeoTIFF 6 路网密度 ASTER GDEM v3 30 m分辨率数字高程数据 GeoTIFF 7 植被覆盖度 Landsat 8 OLI_TIRS遥感影像 Tif 8 地质灾害 云南省重点区域地质灾害精细化调查与风险评价（2022） MapGIS 
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表 2 评价因子分级区间信息量值和加权信息量值
Table 2. Information value and weighted information value for classification intervals of evaluation factors
评价因子 分级区间 信息量值 权重 加权信息量 坡度/（°） [0,9） 0.1667 0.1028 0.0171 [9,17） 0.2674 0.0275 [17,24） − 1.5160 − 0.1558 [24,31） − 1.9337 − 0.1988 [31,40） − − [40,78] − − 地形起伏度/m [0,6） − 0.1311 0.1152 − 0.0151 [6,12） 0.6128 0.0706 [12,18） − 0.1752 − 0.0202 [18,25） − 0.6855 − 0.0790 [25,35） − 1.2903 − 0.1486 [35,147] − − 工程岩组 1 − 0.0905 0.1416 − 0.0128 2 − 0.9164 − 0.1298 3 0.1037 0.0147 4 0.1512 0.0214 5 − 0.4600 − 0.0651 6 2.1236 0.3007 7 − − 8 − − 距断层距离/m 0～400 0.2333 0.1269 0.0296 400～800 0.0164 0.0021 800～ 1200 0.0320 0.0041 1200 ～1600 − 0.6435 − 0.0817 ＞ 1600 − 0.2213 − 0.0281 河网密度 [0,48） − 0.1270 0.1275 − 0.0162 [48,111） 0.3167 0.0404 [111,184） − 0.2150 − 0.0274 [184,293） − 0.3227 − 0.0411 [293,618] − 0.9024 − 0.1151 路网密度 [0,158） − 1.4474 0.2101 − 0.3041 [158,222） − 0.0137 − 0.0029 [222,280） 0.0063 0.0013 [280,350） 0.1947 0.0409 [350,590] 0.3860 0.0811 植被覆盖度 [0,0.2） 0.5733 0.1759 0.1009 [0.2,0.4） 0.5212 0.0917 [0.4,0.6） 0.0344 0.0061 [0.6,0.8） − 0.1182 − 0.0208 [0.8,1.0） − 1.4406 − 0.2534
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表 3 信息量模型各评价分区面积和地质灾害隐患点占比
Table 3. Aera and proportion of geological hazard points in each evaluation zone using the information value model
易发性分区 隐患点个数
/个分区面积
/km2隐患点比例
/%分区比例
/%隐患点
密度极高易发区 108 703.01 61.36 34.99 1.754 高易发区 36 634.70 20.45 31.59 0.647 中易发区 25 465.71 14.20 23.18 0.613 低易发区 7 205.59 3.98 10.23 0.389
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表 4 随机森林赋权信息量模型各评价分区面积和地质灾害隐患点占比
Table 4. Aera and proportion of geological hazard points in each evaluation zone using the random forest weighted information value model
易发性分区 隐患点个数
/个分区面积
/km2隐患点比例
/%分区比例
/%隐患点
密度极高易发区 112 663.63 63.64 33.03 1.926 高易发区 40 668.09 22.73 33.25 0.683 中易发区 23 428.31 13.07 21.32 0.613 低易发区 1 248.98 0.57 12.39 0.046
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