滑坡是世界上最具破坏性的自然灾害之一。滑坡灾害的准确建模和预测已被用作滑坡灾害防治的重要工具。目的探讨耦合模型在滑坡敏感性评价中的应用。本文以威信县为研究对象。首先,根据构建的滑坡目录数据库,研究区域有345次滑坡。选择了12个环境因素,包括地形(海拔,斜坡,斜坡方向,平面曲率,和轮廓曲率),地质结构(地层岩性和与断裂带的距离),气象水文学(年平均降雨量和与河流的距离),和土地覆盖(NDVI,土地利用,和到道路的距离)。然后,单一模型(逻辑回归,支持向量机,和随机森林)和耦合模型(IV-LR,IV-SVM,IV-RF,FR-LR,FR-SVM,和FR-RF)基于信息量和频率比构建,并对模型的准确性和可靠性进行了比较分析。最后,讨论了最优模型下环境因素对滑坡敏感性的影响。结果表明,9种模型的预测精度在75.2%(LR模型)到94.9%(FR-RF模型)之间,耦合精度普遍高于单一模型。因此,耦合模型在一定程度上提高了模型的预测精度。FR-RF耦合模型的精度最高。在最优模型FR-RF下,距离道路,NDVI,土地利用是最重要的三个环境因素,占20.15%,13.37%,和9.69%,分别。因此,威信县有必要加强对道路附近山区和植被稀疏地区的监测,以防止人类活动和降雨造成滑坡。
A landslide is one of the most destructive natural disasters in the world. The accurate modeling and prediction of landslide hazards have been used as some of the vital tools for landslide disaster prevention and control. The purpose of this study was to explore the application of coupling models in landslide susceptibility evaluation. This paper used Weixin County as the research object. First, according to the landslide catalog database constructed, there were 345 landslides in the study area. Twelve environmental factors were selected, including terrain (elevation, slope, slope direction, plane curvature, and profile curvature), geological structure (stratigraphic lithology and distance from fault zone), meteorological hydrology (average annual rainfall and distance to rivers), and land cover (NDVI, land use, and distance to roads). Then, a single model (logistic regression, support vector machine, and random forest) and a coupled model (IV-LR, IV-SVM, IV-RF, FR-LR, FR-SVM, and FR-RF) based on information volume and frequency ratio were constructed, and the accuracy and reliability of the models were compared and analyzed. Finally, the influence of environmental factors on landslide susceptibility under the optimal model was discussed. The results showed that the prediction accuracy of the nine models ranged from 75.2% (LR model) to 94.9% (FR-RF model), and the coupling accuracy was generally higher than that of the single model. Therefore, the coupling model could improve the prediction accuracy of the model to a certain extent. The FR-RF coupling model had the highest accuracy. Under the optimal model FR-RF, distance from the road, NDVI, and land use were the three most important environmental factors, ac-counting for 20.15%, 13.37%, and 9.69%, respectively. Therefore, it was necessary for Weixin County to strengthen the monitoring of mountains near roads and areas with sparse vegetation to prevent landslides caused by human activities and rainfall.