不确定PAHT聚类算法在滑坡危险性预测上的应用  

Uncertain PAHT clustering algorithm in landslide hazard prediction application

作　　者：胡健 朱玲 毛伊敏[2] Hu Jian;Zhu Ling;Mao Yimin(Dept. of Information Engineering, College of Applied Science,Jiangxi University of Science & Technology , Ganzhou Jiangxi 341000, China;School of Information Engineering, Jiangxi University of Science & Technology , Ganzhou Jiangxi 341000, China)

机构地区：[1]江西理工大学应用科学学院信息工程系,江西赣州341000 [2]江西理工大学信息工程学院,江西赣州341000

出　　处：《计算机应用研究》2019年第5期1459-1463,共5页Application Research of Computers

基　　金：江西省教育厅科技项目(GJJ151528;GJJ151531);国家自然科学基金资助项目(41562019;41530640);江西省自然科学基金资助项目(20161BAB203093)

摘　　要：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,有效刻画了不确定的雨量数据;并结合层次聚类思想,通过找出最佳阈值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。In the clustering study of landslide prediction, the difficulties of determining the number of clusters which traditio- nal 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 proposed a new clustering algorithm-uncertain PAHT algorithm. The algorithm introduced a kind of uncertain data model called M-D distance, which effectively measured the uncertain rainfall;and based on the hierarchical clustering thinking, through finding the best threshold p * to determine the k value. Contrast experiment in Yan’an Baota district as an example, the experimental results verify the effectiveness of uncertain M-D distance and PAHT algorithm and the feasibility of uncertain PAHT algorithm on the landslide hazard prediction.

关 键 词：不确定数据 聚类算法 危险性预测 滑坡 

分 类 号：TP399[自动化与计算机技术—计算机应用技术] TP391[自动化与计算机技术—计算机科学与技术]