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

Authors:
Hone-Jay Chu;Churn-Jung Liau;Chao-Hung Lin;Bo-Song Su
Affiliations:
Department of Geomatics, National Cheng-Kung University, No. 1, University Road, Tainan City 701, Taiwan;Institute of Information Science, Academia Sinica, No. 128, Academia Road, Section 2, Taipei City 115, Taiwan;Department of Geomatics, National Cheng-Kung University, No. 1, University Road, Tainan City 701, Taiwan;Department of Geomatics, National Cheng-Kung University, No. 1, University Road, Tainan City 701, Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

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Abstract

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.