Typhoon Track Prediction by a Support Vector Machine Using Data Reduction Methods

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.