水土保持研究

为了客观评价滑坡影响因子的贡献度和构建滑坡预测模型,以滑坡灾害发生较多的攀枝花市为研究区,通过筛选后选取高程、坡度、坡向、土地利用类型、归一化植被指数(NDVI)和人口密度6项因子作为滑坡易发性的评价指标; 基于最大熵(maxEnt)模型和ArcGIS空间分析模块对研究区滑坡易发性进行了定量预测和分析研究。结果表明:maxEnt模型在研究区滑坡易发性研究方面的适用性等级为优秀(AUC=0.96),Kappa系数为0.86; 随机选取75%的数据集用于训练模型,其余25%用于验证模型,得到的AUC值最稳定且精度最高,模型预测可信度最高; 研究区高易发生和极易发生区分别占总面积的2.57%,0.80%,主要分布在人口比较密集的东部和西部地区,部分沿着金沙江、雅砻江、巴关河、安宁河和主要道路两侧发育; 植被覆盖度和坡度是决定研究区滑坡易发性空间分布格局最重要的环境影响因子。

In order to objectively evaluate the contribution of landslide impact factors and build landslide prediction model, Panzhihua City, which has more landslide disasters, was selected as the research area in this study. Six factors including elevation, slope, slope direction, land use, normalized difference vegetation index(NDVI)and population density were selected as evaluation indexes for landslide susceptibility in this paper. Based on the maximum entropy(maxEnt)model and ArcGIS spatial analysis module, the landslide susceptibility in the study area was predicted and analyzed quantitatively. The results show that the applicability of the maxEnt model based on the object unit in the study area for landslide susceptibility is rated as excellence(AUC=0.96), and the Kappa coefficient is 0.86; the AUC value is the most stable and accurate, and the model prediction has the highest credibility when 75% of the data sets are randomly selected to train the model, and the remaining 25% are used to verify the model; The high-prone and high-prone areas in the study area account for 2.57% and 0.80% of the total area, respectively, which mainly distribute in the densely populated eastern and western areas, along the main roads of Jinsha River, Yalong River, Banguan River, Anning River and Panzhihua City; among the influencing factors of landslide susceptibility, vegetation cover and slope are the most important geographical environment factors that determine the spatial distribution pattern of landslide susceptibility in this study area.