基于信息量模型的地质灾害易发性评价:以川东南古蔺县为例

地质灾害威胁着山区人民生命财产安全, 进行地质灾害易发性评价有助于山区城镇进行规划与建设时规避灾害风险。以川东南古蔺县为例, 基于ArcGIS空间分析获取了研究区高程、坡度、岩性、斜坡结构、植被指数、距断层距离和距道路距离7个评价因子, 采用信息量模型分别对滑坡和崩塌灾害进行易发性评价后, 进一步利用ArcGIS单元统计功能对比了滑坡和崩塌易发性的信息量值, 选取相对更大的信息量值作为该栅格的最终信息量值, 绘制了研究区综合地质灾害易发性图, 利用自然断点法将古蔺县按信息量值的大小划分为极低、低、中、高和极高易发区。结果表明: 地质灾害主要分布在断层和道路附近, 断层和人类工程活动是造成研究区地质灾害频发的主要原因; 高易发区与极高易发区面积之和为1 315.62 km2, 占全区总面积的41.32%;预测模型性能经ROC曲线检验, AUC值为0.812 5, 说明栅格最大值法预测的古蔺县综合地灾易发性效果良好。 

Susceptibility assessment of geological disasters based on an information value model: A case of Gulin County in Southeast Sichuan

Geological disasters threaten the safety of people's lives and property in mountainous areas. The assessment of the susceptibility to geological disasters will help mountainous towns carry out planning and construction to avoid disaster risks. Gulin County in Southeast Sichuan was chosen as the study area. First, based on the ArcGIS spatial analysis, we select the seven evaluation factors of elevation, slope, lithology, slope structure, vegetation index, distance from fault and distance from road and use the information value model to assess the susceptibility of landslide and rockfall disasters. Furthermore, the ArcGIS cell statistics function was used to compare the information value of landslide and rockfall susceptibility, and a relatively larger information value was selected as the final information value of the raster unit to draw the comprehensive geological hazard susceptibility map of the study area. The susceptibility of geological disasters in Gulin County is divided into extremely low, low, medium, high and extremely high susceptibility zones by the natural breaks method. The results show that geological disasters are mainly distributed near faults and roads. Faults and human engineering activities are the main reasons for the frequent occurrence of geological disasters in the study area. The total area of high and extremely high susceptibility zones is 1 315.62 km2, accounting for 41.32% of the total area. The performance of the prediction model is tested by the ROC curve, and the AUC is 0.812 5, indicating that the comprehensive disaster susceptibility of Gulin County predicted by the raster maximum method is good.