Comparative analyses on susceptibility of cutting slope landslides in southern Jiangxi using different models
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摘要:
赣南地区滑坡灾害点多、面广、规模小，具有群发性和突发性的特点，90%以上的滑坡是因人工切坡导致的。为研究赣南地区小型削方滑坡对易发性评价模型的适用性，以赣州市于都县银坑镇为例，基于野外地质调查成果，并利用地理探测器，选取坡度、坡体结构、岩组、断层、道路、植被等6个评价指标，分别选用信息量模型、人工神经网络模型、决策树模型和逻辑回归模型开展易发性评价。结果表明：信息量、人工神经网络、决策树和逻辑回归等模型得到的AUC值分别为0.800、0.708、0.672和0.586，信息量模型所得的易发性结果与研究区滑坡实际分布情况较吻合，高易发区和中易发区滑坡占比近80%。信息量模型较其他三个模型，更适合于赣南地区小型削方滑坡易发性评价，评价结果对该地区地质灾害易发性评价模型选取提供了参考与借鉴。
Abstract:There are many landslide disasters in southern Jiangxi, with a wide area and a small scale, and are characterized by mass and suddenness. More than 90% of landslides are caused by artificial slope cutting. In order to study the applicability of the susceptibility evaluation model for cutting slope landslides caused by cutting slopes in southern Jiangxi, taking Yinkeng Town, Yudu County, Ganzhou City as an example, based on the results of field geological surveys, and using GeoDetectors, the slope, the slope structure, rock formation, fault, road, and vegetation, were selected to carry out landslide susceptibility assessment by using the information value model (I), artificial neural network model (ANN), decision tree model (DT) and Logic regression model respectively. The results show that the AUC values obtained from information value model, artificial neural network model, decision tree model and logistic regression model are 0.800, 0.708, 0.672 and 0.586, respectively. The susceptibility results obtained by the information value model are in good agreement with the actual distribution of landslides in the study area. The specific value of the proportion of landslides in high-prone areas and medium-prone areas exceeds 80%. The information model is more suitable for the landslide susceptibility assessment under cutting slope in southern Jiangxi than the other three models. The assessment results provide a reference for the selection of the assessment model for the geohazard susceptibility in this region.
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Key words:
- cutting slope /
- GeoDetectors /
- information value /
- artificial neural network /
- decision tree /
- logistic regression
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表 1 地理探测器得到11个指标的q值
Table 1. The normalized weight values of 11 indicators
指标 距道路距离 岩组 坡体
结构坡度 距断层距离 植被
覆盖率坡向 高程变异系数 粗糙度 曲率 地表切割深度 q值 0.2897 0.2767 0.1203 0.1102 0.0807 0.0727 0.0152 0.0122 0.0120 0.0092 0.0011 排序 1 2 3 4 5 6 7 8 9 10 11 
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表 2 评价指标各自信息量值
Table 2. Each information value of evaluation index
评价指标 评价指标子类 信息量值 坡度/（°） 0～7 −0.439 7～17 0.186 17～26 0.304 26～36 −0.268 >36 0 坡体结构 碎石土质边坡 −0.115 岩质−顺向坡 0.145 岩质−逆向坡 −0.147 岩质−斜向破 0.147 工程地质岩组 多层含砾黏土、粉质黏土 −0.458 较坚硬−坚硬的变质砂岩、变质粉砂岩、
千枚岩等组0.140 坚硬花岗岩组 0.508 较坚硬的波状复成份砾岩、安山岩岩组 2.457 较硬、较软的砾岩、粉砂岩、页岩等组 −0.942 较软弱−较坚硬石英砾岩、
砂岩、粉砂岩、泥岩等组0.888 坚硬石英砾岩、砂砾岩、粉砂岩等组 −1.974 软硬相间的砾岩、粉砂岩夹煤层 −0.060 距断层距离/m <100 0.395 100～200 0.237 200～300 0.461 300～400 −0.093 400～500 −0.078 >500 −0.058 距道路距离/m <50 −0.076 50～100 0.772 100～200 1.076 200～500 1.109 >500 −0.508 植被 高植被覆盖率 −0.412 较高植被覆盖率 −0.168 中植被覆盖率 −0.313 较低植被覆盖率 0.007 低植被覆盖率 −0.206 极低植被覆盖率 0.161
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表 3 滑坡点在各个分区所占比例
Table 3. The proportion of disaster points in each partition
易发性
分区灾害点在各个分区所占比例/% I ANN DT LR 非 5.6 6.5 16.5 18.6 低 16.8 16.6 23.4 20.8 中 59.2 48.1 41.1 38.9 高 18.4 28.8 19.0 21.7
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