Evaluation of geohazard susceptibility based on information value model and information value-logistic regression model: A case study of the central mountainous area of Hainan Island
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摘要: 地质灾害易发性评价作为地质灾害风险评价的基础，运用定量化的数学统计原理对地质灾害易发性进行研究能够客观准确地反映地质灾害发生的概率。文章以海南岛地质灾害最为发育的五指山市为例，选择断裂、岩土体、坡度、地形起伏度、海拔高程变异系数、归一化植被指数(NDVI)、降雨量、水系、公路、曲率值为评价指标，依托详查资料和遥感、地形数据，采用信息量模型和信息量-逻辑回归模型对研究区地质灾害易发性进行评价研究，评价结果经敏感性检验、频率比检验后表明：高易发区主要分布于山区公路和水系两侧沿线，极低易发区主要位于河谷不发育、人类工程活动较少的丘陵低山地带。两种模型的ROC曲线下面积值(AUC)分别为0.897和0.896，表明预测精度满足易发性评价要求。降雨、高程变异系数、公路等评价因子对地质灾害易发性起较强的控制作用。信息量-逻辑回归模型具有更高的可靠性和精准度，研究成果将为该地区地质灾害风险评价提供科学有效的判别方法和预测途径。Abstract: As the basis of geohazard risk evaluation, the geohazard susceptibility evaluation can objectively and accurately reflect the probability of geological hazard occurrence by using quantitative mathematical statistics. This article takes Wuzhishan city, where occurs the most geohazards in Hainan Island, as an example. Factors including structure, rock formations, slopes, topographic undulations, altitude variation coefficients, normalized differential vegetation index (NDVI), rainfall, river systems, roads, and curvatures were selected as evaluation indicators and applied in both information value model and information value-logistic regression model. In the end, by comparing and analyzing the accuracy and adaptability of both models, the article ends with the conclusion that the high-prone areas are mainly distributed along roads and rivers in the mountainous areas, and the extremely low-prone areas are mainly located in the areas where have no rivers, valleys and human activities. In addition, the results also revealed that the prediction accuracy meets the requirements of susceptibility evaluation owing to the high AUC (area under the curve) values occupying 0.897 and 0.896 respectively in both models. Evaluation factors such as rainfall, elevation variation coefficient and highway play a remarkable role on the development of geohazards. Furthermore, it is indicated by experiments that the information value-logistic regression model has better prediction accuracy than the other. The research results provide a scientific and effective discrimination method and a prediction approach for geohazard risk evaluation in this area.
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Key words:
- information value /
- logistic regression /
- susceptibility /
- geohazard /
- coupled model /
- Wuzhishan city
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图 4 地质灾害易发性影响因子分级与地质灾害分布统计图
a—断裂；b—坡度；c—地形起伏度；d—高程变异系数；e—岩土体；f—水系；g—公路；h—NDVI；i—曲率；j—降雨量
Figure 4. Statistical chart of geological hazard susceptibility influencing factor classification and geological hazard distribution
(a) Fracture; (b) Slope; (c) Terrain undulations; (d) Coefficient of variation for elevation; (e) Rock and soil; (f) Water systems; (g) Roads; (h) NDVI; (i) Curvature; (j) Rainfall
图 6 基于信息量-逻辑回归模型易发性图
Figure 6. Vulnerability graph based on information value-logistic regression model
表 1 数据来源统计(夏南等，2014)
Table 1. Statistics of data sources (Xia et al, 2014)
序号 数据类型 来源 1 灾害点本底数据 1∶5万地质灾害详查项目 2 NDVI 高分2遥感影像 3 岩土体 海南岛1∶25万地质图 4 地形、坡度以及起伏度等 DEM地形数据 5 构造断裂 海南岛1∶50万地质构造图 
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表 2 评价因子信息量值统计
Table 2. Information value of evaluation factors
评价因子 分级类别 Ni/Si N/S 信息值 评价因子 分级类别 Ni/Si N/S 信息值 断裂/m 0~1000 0.28 0.21 0.1274 高程变异系数 0~0.01 0.21 0.21 -0.0015 1000~2000 0.18 0.21 -0.0608 0.01~0.02 0.14 0.21 -0.3931 2000~3000 0.13 0.21 -0.1984 0.02~0.03 0.30 0.21 0.3594 3000~4000 0.18 0.21 -0.0590 0.03~0.04 0.33 0.21 0.4498 4000~5000 0.21 0.21 0.0045 0.04~0.05 0.26 0.21 0.2107 >5000 0.22 0.21 0.0205 >0.05 0.31 0.21 0.3820 岩土体 厚—薄层状坚硬—软弱砂岩夹泥岩组 0 0.21 0 水系/m 0~60 0.05 0.21 -1.5121 60~120 0.40 0.21 0.6444 块状坚硬花岗岩、闪长岩岩组 0.13 0.21 -0.4911 120~180 0.59 0.21 1.0321 块状—薄层状坚硬—软弱变质石英砂岩和片状、板状变质岩组 0.03 0.21 -2.0229 180~240 0.43 0.21 0.7203 240~300 0.46 0.21 0.7794 砂卵石、中粗砂、黏性土多层土体 0.98 0.21 1.5387 >300 0.26 0.21 0.2090 公路/m 0~60 0.01 0.21 -2.7813 块状坚硬—较坚硬中酸性熔岩组 0 0.21 0 60~120 0.89 0.21 1.4436 120~180 0.41 0.21 0.6789 坡度/(°) 0~10 0.34 0.21 0.4933 180~240 0.15 0.21 -0.3597 10~20 0.33 0.21 0.4539 240~300 0.05 0.21 -1.3625 20~30 0.11 0.21 -0.6366 >300 0.08 0.21 -0.9478 30~40 0.02 0.21 -2.2386 NDVI 0~0.36 1.00 0.21 1.5607 40~50 0 0.21 0 0.36~0.57 0.63 0.21 1.1059 >50 0 0.21 0 0.57~0.64 0.28 0.21 0.2715 地形起伏度/m 0~15 0.39 0.21 0.6237 0.64~0.70 0.12 0.21 -0.5584 15~30 0.30 0.21 0.344 0.70~0.84 0.10 0.21 -0.7561 30~45 0.11 0.21 -0.6183 >0.84 0.29 0.21 0.3285 45~60 0.02 0.21 -2.606 曲率值 < -30 0.07 0.21 -1.0784 60~75 0 0.21 0 -15 0.14 0.21 -0.3853 >75 0 0.21 0 -15 0.24 0.21 0.1573 降雨量/mm 1540~1740 0.18 0.21 -0.1374 0~15 0.22 0.21 0.0521 1740~1940 0.13 0.21 -0.4659 15~30 0.08 0.21 -0.9933 1940~2140 0.26 0.21 0.2283 >30 0 0.21 0
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表 3 方差膨胀因子(VIF)与容许度
Table 3. The variance inflation factor (VIF) and the tolerance
因子 岩土体 公路 水系 NDVI 坡度 起伏度 曲率 断裂 降雨量 高程变异系数 容差 0.892 0.998 0.886 0.898 0.992 0.901 0.955 0.996 0.983 0.948 VIF 1.121 1.002 1.129 1.114 1.008 1.110 1.047 1.004 1.017 1.055
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表 4 各影响因子之间的相关系数矩阵
Table 4. Correlation coefficient matrix between various impact factors
因子 岩土体 公路 水系 NDVI 坡度 起伏度 曲率 断裂 降雨量 高程变异系数 岩土体 1.000 0.004 0.191 0.217 0.031 0.107 0.052 0.008 0.041 0.026 公路 1.000 0.021 0.015 -0.001 0.010 -0.013 0.010 -0.007 0.023 水系 1.000 0.102 0.055 0.153 0.075 -0.027 -0.058 0.120 NDVI值 1.000 0.020 0.125 0.062 0.044 -0.019 0.113 坡度 1.000 -0.041 0.005 -0.021 -0.022 0.008 起伏度 1.000 0.166 -0.004 -0.059 -0.106 曲率 1.000 -0.001 -0.101 -0.033 断裂 1.000 0.020 -0.008 降雨量 1.000 0.001 高程变异系数 1.000
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表 5 逻辑回归分析参数
Table 5. Data of preliminary logistic regression analysis
因子 B 标准误差 瓦尔德 自由度 显著性 Exp(B) 公路 0.873 0.090 95.001 1 0.000 2.395 水系 0.647 0.103 39.708 1 0.000 1.910 NDVI 0.492 0.135 13.235 1 0.000 1.636 起伏度 0.518 0.167 9.641 1 0.002 1.679 断裂 0.450 0.288 2.447 1 0.018 1.569 降雨量 1.571 0.501 9.832 1 0.002 4.813 高程变异系数 0.933 0.287 10.539 1 0.001 2.541 常量 -2.095 0.147 204.027 1 0.000 0.123 注：B表示的逻辑回归系数；Exp(B)表示因素变化一个单位，地质灾害发生的概率增加的倍数
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表 6 评价因子回归系数与权重
Table 6. Regression coefficient and weight of evaluation factors
因子 公路 水系 NDVI 起伏度 断裂 降雨量 高程变异系数 常量 回归系数(B) 0.873 0.647 0.492 0.518 0.45 1.571 0.933 -2.095 权重 0.16 0.12 0.09 0.09 0.08 0.29 0.17 -
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表 7 五指山市地质灾害分区结果合理性检验表
Table 7. Checklist for the rationality of the classification results of the geohazards in Wuzhishan
模型 易发性分区 分区个数 个数占比(Q) 分区面积/km2 面积占比(A) 比值(R=Q/A) 信息量模型 极低易发区(Ⅰ) 1 2.56% 200.00 17.77% 0.14 低易发区(Ⅱ) 2 5.13% 389.86 34.64% 0.15 中易发区(Ⅲ) 9 23.07% 333.04 29.59% 0.78 高易发区(Ⅳ) 27 69.24% 202.46 18.00% 3.84 信息量逻辑回归模型 极低易发区(Ⅰ) 0 0.00% 339.90 30.05% 0.00 低易发区(Ⅱ) 1 2.56% 317.29 28.05% 0.09 中易发区(Ⅲ) 9 23.08% 263.97 23.34% 0.99 高易发区(Ⅳ) 29 74.36% 209.88 18.56% 4.01
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