不确定GM-CFSFDP聚类算法在滑坡危险性预测中的应用

针对滑坡危险性预测中降雨等不确定诱发因素难以有效处理，CFSFDP算法需要人工尝试设置密度阈值以及对大规模数据集无法进行准确聚类等问题，为了提高滑坡危险性预测准确度，提出一种基于网格与类合并的不确定CFSFDP （简称不确定GM-CFSFDP）聚类算法.该算法首先引入不确定数据处理方法，设计了E-ML距离公式，有效刻画降雨不确定因素；其次通过网格划分的思想把大规模数据集划分到多个网格空间中，实现大规模数据有效编码；计算网格平均密度，建立网格密度阈值分布模型，动态获得网格密度阈值；最后利用层次聚类思想对关联性较高的类进行合并，构建不确定GM-CFSFDP算法模型，在延安宝塔区进行滑坡实例验证.实验结果表明不确定GM-CFSFDP聚类算法获得较高的预测精度，从而验证了该算法在滑坡危险性预测中的可行性和先进性.

Application of Uncertain GM-CFSFDP Clustering Algorithm in Landslide Hazard Prediction

Since the rainfall and other uncertainties are difficult to effectively deal with in landside hazard prediction, as well as the density threshold in CFSFDP algorithm is required to be set manually and its low accuracy for large-scale data clustering, in order to improve the prediction accuracy, this study proposed an uncertain CFSFDP algorithm based on Grid and Merging clusters (uncertain GM-CFSFDP). Firstly, the E-ML distance formula based on uncertain data processing method is designed to effectively describe the uncertain factors of rainfall. Secondly, the idea of meshing is used to effectively encode the large-scale data by dividing it into multiple grid spaces. The average density of the mesh is calculated to establish the grid density threshold distribution model and obtain the grid density threshold dynamically. Finally, the hierarchical clustering idea is used to merge the higher association class and the uncertain GM-CFSFDP algorithm model is established. The experiments conducted in the Baota district of Yan''an show that the uncertain GM-CFSFDP clustering algorithm achieves a higher prediction accuracy and proves the feasibility and advancement of the algorithm in landslide hazard prediction.