Because of the strong dependence on the values for the input parameters and the cluster shape, as well as the difficulties in quantifying the precipitation in constructing landslide susceptibility maps by employing existing clustering algorithms, we propose a novel method based on an Ordering Points to Identify the Clustering Structure (OPTICS) algorithm using the Hausdorff distance (OA-HD). The OA-HD algorithm distributes mapping units into many subclasses with similar characteristic values for topography and geology. To obtain more optimal subclasses, the HD was adopted to quantify precipitation. The K-medoids algorithm grouped these subclasses into five susceptibility levels according to the values of landslide density in each subclass. Applying the innovative integrated algorithms to the study area significantly improves the landslide susceptibility assessment, especially in a large study area. The method suggests new insights for better assessing landslide susceptibility in a large study area.

由于对输入参数和簇形状的值的依赖性很大，并且在利用现有聚类算法构建滑坡敏感性图时难以量化降水量，因此我们提出了一种基于排序点的新方法来识别滑坡敏感性。使用Hausdorff距离（OA-HD）的聚类结构（OPTICS）算法。OA-HD算法将映射单元分布到具有相似的地形和地质特征值的许多子类中。为了获得更多的最佳子类，采用了高清量化降水。K-medoids算法根据每个子类中的滑坡密度值将这些子类分为五个敏感性级别。在研究区域中应用创新的集成算法可以显着改善滑坡敏感性评估，尤其是在大型研究区域中。该方法提供了新的见解，可用于更好地评估大型研究区域中的滑坡敏感性。