不确定PAHT聚类算法在滑坡危险性预测上的应用

针对滑坡预测聚类研究中由于难以确定传统聚类算法需要预先设置的簇个数和无法精准衡量不确定因素降雨量导致预测效果欠佳的问题，提出一种新的聚类算法—不确定PAHT（partition algorithm on the hierarchical thinking）算法，该算法引入一种不确定数据模型——M-D距离，其有效刻画了不确定的雨量数据；并结合层次聚类思想，通过找出最佳阙值p*自动确定k值。以延安宝塔区为实例进行对比实验，实验结果验证了不确定M-D距离和PAHT算法的有效性及不确定PAHT算法在滑坡危险性预测上的可行性。

Uncertain PAHT clustering algorithm in landslide hazard prediction application

In the clustering study of landslide prediction, the difficulties of determining the number of clusters which traditional clustering algorithm needs to set in advance and accurately measuring the important factor of Landslide induced-rainfall leads to bad prediction effect. Therefore, this paper proposes a new clustering algorithm-Uncertain PAHT algorithm , the algorithm introduces a kind of uncertain data model called M-D distance, which effectively measure the uncertain rainfall; and based on the hierarchical clustering thinking, through finding the best threshold p* to determine the k value. Contrast experiment in Yenan Baota district as an example, the experimental results verified the effectiveness of uncertain M-D distance and PAHT algorithm and the feasibility of uncertain PAHT algorithm on the landslide hazard prediction.