Authors:
John Chien-Han Tseng
1
;
Hsing-Kuo Pao
2
and
Christos Faloutsos
3
Affiliations:
1
Central Weather Bureau, Taiwan
;
2
National Taiwan University of Science & Technology, Taiwan
;
3
Carnegie Mellon University, United States
Keyword(s):
Typhoon tracks, Trajectory data, ENSO, La Ni˜na, Tri-plots, Markov chain, Dissimilarity measure, Isomap, SSVM.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Clustering and Classification Methods
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Mining Multimedia Data
;
Symbolic Systems
Abstract:
We aim at understanding typhoon tracks by classifying them into the ENSO and La Niña types. Two methods, namely, tri-plots and Markov chain combined with a novel dissimilarity measure for trajectory data are proposed in this work. The calculation of the tri-plots can help us to separate ENSO from La Niña year typhoon tracks with the training error about 0.023 to 0.268 and the test error about 0.271 to 0.334. The Markov chain based dissimilarity measure, combined with the SSVM classifier can help us to classify tracks with the training error around 0.031 to 0.173 and the test error around 0.181 to 0.287. Moreover, for the purpose of visualization, the tri-plots or Markov chain-based method maps the typhoon track data into low dimensional space. In the space, the typhoon tracks of small dissimilarity should be regarded as one group. The map can be very helpful for catching the hidden pattern of ENSO and La Niña atmospheric circulation for establishing typhoon databases. In general, we
believe that tri-plots and Markov chain-based method are useful tools for the typhoon track classification problem and should merit further investigation in related research community.
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