A New Data Mining Model for Hurricane Intensity Prediction

Abstract

This paper proposes a new hurricane intensity prediction model, WFL-EMM, which is based on the data mining techniques of feature weight learning (WFL) and Extensible Markov Model (EMM). The data features used are those employed by one of the most popular intensity prediction models, SHIPS. In our algorithm, the weights of the features are learned by a genetic algorithm (GA) using historical hurricane data. As the GAs fitness function we use the error of the intensity prediction by an EMM learned using given feature weights. For fitness calculation we use a technique similar to k-fold cross validation on the training data. The best weights obtained by the genetic algorithm are used to build an EMM with all training data. This EMM is then applied to predict the hurricane intensities and compute prediction errors for the test data. Using historical data for the named Atlantic tropical cyclones from 1982 to 2003, experiments demonstrate that WFL-EMM provides significantly more accurate intensity predictions than SHIPS within 72 hours. Since we report here first results, we indicate how to improve WFL-EMM in the future.