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Typhoon track prediction by a support vector machine using data reduction methods

Title
Typhoon track prediction by a support vector machine using data reduction methods
Authors
Song H.-J.Huh S.-H.Kim J.-H.Ho C.-H.Park S.-K.
Ewha Authors
박선기
SCOPUS Author ID
박선기scopus
Issue Date
2005
Journal Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN
0302-9743JCR Link
Citation
vol. 3801 LNAI, pp. 503 - 511
Indexed
SCOPUS WOS scopus
Abstract
Typhoon track prediction has mostly been achieved using numerical models which include a high degree of nonlinearity in the computer program. These numerical methods are not perfect and sometimes the forecasted tracks are far from those observed. Many statistical approaches have been utilized to compensate for these shortcomings in numerical modeling. In the present study, a support vector machine, which is well known to be a powerful artificial intelligent algorithm highly available for modeling nonlinear systems, is applied to predict typhoon tracks. In addition, a couple of input dimension reduction methods are also used to enhance the accuracy of the prediction system by eliminating irrelevant features from the input and to improve computational performance. © Springer-Verlag Berlin Heidelberg 2005.
DOI
10.1007/11596448_74
ISBN
3540308180; 9783540308188
Appears in Collections:
엘텍공과대학 > 환경공학전공 > Journal papers
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