摘要
针对闪电定位仪中庞大而杂乱的定位数据,提出一种基于改进DBSCAN聚类算法(IDBSCAN)进行闪电聚类分析的方法。该方法依据闪电定位系统中的实时监控数据,搜索闪电密度大于阈值范围的地闪点,建立密度可达最大值的地闪聚类簇,并找到该簇类中的核心地闪点。同时,应用邻接表结构对DBSCAN算法进行改进,使得初始地闪数据的搜索集的建立时间和空间得到大大减少。在聚类分析结果基础上,对核心地闪点的移动路径进行拟合,从而预报下一时刻的核心地闪点位置。实验证明,将IDBSCAN算法应用在闪电临近预报中是有效的。
Against the massive monitoring data of lightning locating system,a lightning nowcasting model based on Improved Density-Based Spatial Clustering of Application with Noise（IDBSCAN） clustering algorithm was put forward.Based on the lightning location data in real-time monitoring system,this method searched for lightning-density flash point greater than the threshold value of the land,built the cluster with up to the maximum ground flash density,and located the core of the cluster.Besides,with the application of adjacency list search algorithm,time and space consumed for the initial search set of lightning data had been greatly reduced.Furthermore,using regression fitting algorithm,the proposed algorithm can predict the path of movement of lightning cluster.The experimental results show that IDBSCAN algorithm used in the lightning nowcasting is effective.
出处
《计算机应用》
CSCD
北大核心
2012年第3期847-851,共5页
journal of Computer Applications
基金
国家自然科学基金资助项目(50875175)
天津市自然科学基金资助项目(10JCZDJC23400)
江苏高校优势学科建设工程项目
关键词
闪电临近预报
定位资料
DBSCAN算法
邻接表
空间聚类
lightning nowcasting
location information
DBSCAN algorithm
adjacency list
spatial clustering
