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    Abstract
2016 (Vol. 13, Issue: 8)
Research Article

Applying Machine Learning Methods for Predicting Tropical Cyclone Rapid Intensification Events

1Hadil Shaiba and 2Michael Hahsler
1Department of Computer Science and Engineering, Southern Methodist University, Dallas, TX 75275, United States
2Department of Engineering Management, Information and System, Southern Methodist University, Dallas, TX 75275, United States
 

DOI: 10.19026/rjaset.13.3050
Submitted Accepted Published
April ‎2, ‎2016 June ‎25, ‎2016 October 15, 2016

  How to Cite this Article:

1Hadil Shaiba and 2Michael Hahsler, 2016. Applying Machine Learning Methods for Predicting Tropical Cyclone Rapid Intensification Events.  Research Journal of Applied Sciences, Engineering and Technology, 13(8): 638-651.

DOI: 10.19026/rjaset.13.3050

URL: http://www.maxwellsci.com/jp/mspabstract.php?jid=RJASET&doi=rjaset.13.3050

Abstract:



The aim of this study is to improve the intensity prediction of hurricanes by accounting for Rapid Intensification (RI) events. Modern machine learning methods offer much promise for predicting meteorological events. One application is providing timely and accurate predictions of Tropical Cyclone (TC) behavior, which is crucial for saving lives and reducing damage to property. Current TC track prediction models perform much better than intensity (wind speed) models. This is partially due to the existence of RI events. An RI event is defined as a sudden change in the maximum sustained wind speed of 30 knots or greater within 24 hours. Forecasting RI events is so important that it has been put on the National Hurricane Center top forecast priority list. The research published published on usingmachinelearning methods for RI prediction is currently very limited. In this study, we investigate the potential of popular machine learning methods to predict RI events. The evaluated models include support vector machines, logistic regression, na´ve-Bayes classifiers, classification and regression trees and a wide range of ensemble methods including boosting and stacking. We also investigate dimensionality reduction and feature selection and we address class imbalance using the Synthetic Minority Over-sampling Technique (SMOTE). The evaluation shows that some of the investigated models improve over the current operational Rapid Intensification Index model finally; we use RI predictions to make improved storm intensity predictions.

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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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