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

Hee Jun Song, Sung Hoe Huh, Joo Hong Kim, Chang Hoi Ho, Seon Ki Park

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Scopus citations

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.

Original languageEnglish
Title of host publicationComputational Intelligence and Security - International Conference, CIS 2005, Proceedings
Pages503-511
Number of pages9
DOIs
StatePublished - 2005
EventInternational Conference on Computational Intelligence and Security, CIS 2005 - Xi'an, China
Duration: 15 Dec 200519 Dec 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3801 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Conference on Computational Intelligence and Security, CIS 2005
Country/TerritoryChina
CityXi'an
Period15/12/0519/12/05

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