Landslide susceptibility evaluation of Sinan County using logistics regression model and 3S technology
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摘要: 区域滑坡易发性评价对滑坡灾害防治具有重要意义,贵州省思南县由于其特殊的自然地理和地质条件,受滑坡地质灾害的影响非常严重,因此,非常有必要对思南县的滑坡易发性进行评价。在滑坡编录的基础上,采用由RS、GIS和GPS组成的3S技术,获取了思南县的数字高程模型、坡度、坡向、剖面曲率、坡长、岩土类型、地表湿度指数、距离水系的距离、植被覆盖度和地表建筑物指数10个滑坡影响因子;再在频率比和相关性分析的基础上,利用逻辑回归模型对思南县的滑坡易发性进行了评价并绘制了易发性分布图。结果表明:利用逻辑回归模型预测思南县滑坡易发性的准确率(AUC值)达到0.797,较为准确地预测出了思南县滑坡分布规律;极高和高滑坡易发区主要分布在高程低于600 m、地表坡度较大且以软质岩类为主的区域;而极低和低滑坡易发区主要分布在高程较高、地表坡度较小且以硬质岩类为主的区域。Abstract: The Sinan County of Guizhou Province, due to the specific and complex physical geography and geological conditions, is seriously affected by the landslide hazards. Hence, it is very necessary to conduct regional landslide susceptibility evaluation for landslide prediction and prevention in the area. This study uses 3S technology:remote sensing (RS), globe position system (GPS) and geographic information system (GIS), to evaluate landslide susceptibility based on the logistic regression (LR) model. The 3S technology is applied to obtain the landslide inventory, condition factors of landslides and other related basic data in Sinan County. About 308 landslides and ten affecting factors are acquired digital elevation model (DEM), slope, aspect, profile curvature, rock types, buffer of fracture lines, modified normalized difference water index (MNDWI), distance to river, normalized difference vegetation index (NDVI) and normalized difference building index (NDBI), using the 3S technology. Then based on the correlation analysis, LR model is used to calculate the landslide susceptibility indexes and map these indexes. Results show that, the area under the curve (AUC) of receiver operating characteristic curve (ROC) is 0.797 using LR model. The landslide distribution characteristics of Sinan County are accurately predicted by the LR model. In addition, the high and very high susceptible areas are mainly distributed in the areas where the DEM are higher than 600 m. In these areas, the slope are relatively great and the rocks are soft. The low and very low susceptible areas are mainly distributed in the areas where the DEM are high, the slopes are relatively low and the rocks are of hard rock class.
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表 1 滑坡易发性评价的影响因子分级体系
Table 1. Classification system of basic influencing factors of landslides in Sinan County
因子类别 影响因子 因子分级 地形地貌 高程/m < 476;476~ < 568;568~ < 648;648~ < 729;729~ < 815;815~ < 911;911~1 021;>1 021 坡度/(°) < 6.11;6.11~ < 10.88;10.88~ < 15.40;15.40~ < 19.91;19.91~ < 24.95;24.95~ < 31.06;31.06~39.29;>39.29 坡向 平坡;正北;东北;正东;东南;正南;西南;正西;西北 剖面曲率 < 2.2;2.2~ < 4.2;4.2~ < 6.4;6.4~ < 8.7;8.7~ < 11.5;11.5~ < 14.9;14.9~19.9;>19.9 坡长/m < 1.3;1.3~ < 2.7;2.7~ < 4.0;4.0~ < 5.5;5.5~ < 7.2;7.2~ < 9.4;9.4~ < 12.1;>12.1 工程地质 岩土类型 软质岩类;硬质岩类;软硬相间岩类;松散岩类 水文环境 MNDWI < 39;39~ < 61;61~ < 83;83~ < 106;106~ < 132;132~ < 163;163~215;>215 距离水系的距离/m < 300;300~ < 600;600~900;>900 m 地表覆盖因子 NDVI < 38;38~ < 93;93~ < 128;128~ < 154;154~ < 176;176~ < 199;199~224;>224 NDBI < 44;44~ < 73;73~ < 100;100~ < 126;126~ < 154;154~ < 183;183~218;>218 表 2 逻辑回归方程中的相关变量
Table 2. Relative variables of the logistic regression model
变量 影响因子 系数值 S.E. Wals df Sig. X1, j 高程 1.508 0.026 3 344.073 1 0.000 X2, j 坡度 1.500 0.032 2 164.334 1 0.000 X3, j 坡向 -0.109 0.138 22.627 1 0.041 X4, j 剖面曲率 1.717 0.093 340.572 1 0.000 X5, j 坡长 0.655 0.037 317.869 1 0.000 X6, j 岩土类型 0.998 0.015 4 298.464 1 0.000 X7, j MNDWI 0.784 0.093 71.343 1 0.000 X8, j 距离水系的距离 2.714 0.081 1 124.072 1 0.000 X9, j NDBI 0.225 0.088 49.622 1 0.000 X10, j NDVI 0.622 0.038 34.364 1 0.000 C 常量 -11.450 0.241 2 263.122 1 0.000 注:S.E.标准误差;Wals.检验因子;df.自由度;Sig.显著性检验 -
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