Integration of fuzzy cluster analysis and kernel density estimation for tracking typhoon trajectories in the Taiwan region

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• 作者：Hone-Jay Chu^a ; Churn-Jung Liau^b ; Chao-Hung Lin^a ; ^{linhung@mail.ncku.edu.tw} ; Bo-Song Su^a
• 关键词：Clustering ; Typhoon trajectory ; k-means ; Fuzzy c-means (FCM) ; Hierarchical clustering ; Normalized cut ; Kernel density estimation (KDE)
• 刊名：Expert Systems with Applications
• 出版年：2012
• 期刊代码：62_09574174
• 类别：et
• 出版时间：August, 2012
• 卷：39
• 期：10
• 页码：9451-9457
• 文件大小：929 K

摘要

Increasing our understanding of typhoon movements remains a priority in the western North Pacific. In this study, the trajectories of typhoons that affected Taiwan between 1986 and 2010 are used for clustering, where each trajectory consists of 6-hourly latitude-longitude positions over two days. We compare the performance of four statistical clustering methods, namely, k-means clustering, fuzzy c-means (FCM) clustering, hierarchical clustering, and normalized cut techniques. The results show that the FCM technique provides sufficient cluster efficiency with a relatively high degree of goodness of fit. FCM identifies six clusters according to the minimum coefficients of variation (CV). The hotspots of the typhoon centers in each cluster are determined by kernel density estimation (KDE). Moreover, the typhoon track belongs to six clusters with different membership degrees in FCM. The typhoon track density map is estimated by combining the KDE hotspot maps associated with the FCM weights. The information could be used in planning for disaster management.