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

Hone-Jay Chu, Churn Jung Liau, Chao-Hung Lin, Bo Song Su

Research output: Contribution to journalArticle

30 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)9451-9457
Number of pages7
JournalExpert Systems With Applications
Volume39
Issue number10
DOIs
Publication statusPublished - 2012 Aug 1

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Cluster analysis
Trajectories
Fuzzy clustering
Disasters
Planning

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Engineering(all)

Cite this

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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.",
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Integration of fuzzy cluster analysis and kernel density estimation for tracking typhoon trajectories in the Taiwan region. / Chu, Hone-Jay; Liau, Churn Jung; Lin, Chao-Hung; Su, Bo Song.

In: Expert Systems With Applications, Vol. 39, No. 10, 01.08.2012, p. 9451-9457.

Research output: Contribution to journalArticle

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