基于随机森林的山西省柳林县黄土滑坡空间敏感性评价

Spatial Sensitivity Evaluation of Loess Landslide in Liulin County,Shanxi Based on Random Forest

基于随机森林模型,以GF-6影像和ALOS DEM数据为基本信息源,结合高程,地形起伏度及地形湿度等11项因子,对山西省柳林县进行滑坡敏感性空间区划。模型精度评价表明:随机森林模型精度为0.75,支持向量机模型精度为0.7,表明随机森林更适合柳林县的滑坡敏感性评价。指标重要性分析结果表明:高程、坡度、距道路距离以及距河流距离,是影响柳林县滑坡发育的主要因素。敏感性空间区划结果表明:高度敏感区约占柳林县总面积的28%,主要分布在三川河流域的南北边界及邻近区域内,其中贾家垣乡分布面积最广。从时间成本、训练难度、稳定度以及精确度考虑,随机森林模型更适合滑坡敏感性评价这类非线性计算问题。

Based on the random forest model, the GF-6 image and ALOS DEM data were used as the basic information source, combined with 11 factors such as elevation, topographic relief and topographic moisture, to spatially zone the landslide sensitivity of Liulin County, Shanxi Province. The evaluation of model accuracy shows that the random forest model has an accuracy of 0.75 and the support vector machine model has an accuracy of 0.7, indicating that the random forest is more suitable for the landslide sensitivity evaluation in Liulin County. The results of the importance analysis of indicators show that: elevation, slope, distance from roads and distance from rivers are the main factors affecting the development of landslides in Liulin County.The results of the sensitivity spatial zoning indicate that the highly sensitive areas account for about 28% of the total area of Liulin County and are mainly distributed within the north and south boundaries of the Sanchuan River Basin and adjacent areas, with Jiajiaguan Township having the most widely distributed area. In terms of time cost, training difficulty, stability and accuracy, the random forest model is more suitable for such non-linear computational problems as landslide sensitivity evaluation.