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, naïve-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.

Keywords:

Ensemble learning, feature selection, feature extraction, machine learning, rapid intensification, SMOTE,

References

1. Breiman, L., 2001. Random forests. Mach. Learn., 45(1): 5-32.
   CrossRef    Direct Link
2. Breiman, L., J. Friedman, C.J. Stone and R.A. Olshen, 1984. Classification and Regression Trees. Wadsworth, Belmont, Calif.
   
3. DeMaria, M., 2013. Ships predictor file. RAMMB Technical Report, Colorado State University, Regional and Mesoscale Meteorology Branch (RAMMB) of NOAA/NESDIS. 2013.
   Direct Link
4. DeMaria, M., M. Mainelli, L.K. Shay, J.A. Knaff and J. Kaplan, 2005. Further improvements to the statistical hurricane intensity prediction scheme (SHIPS). Weather Forecast., 20(4): 531-543.
   CrossRef    Direct Link
5. Dunteman, G.H., 1989. Principal Components Analysis. Sage University Papers Series, Newbury Park, Calif.
   CrossRef    
6. Galar, M., A. Fernandez, E. Barrenechea, H. Bustince and F. Herrera, 2012. A review on ensembles for the class imbalance problem: Bagging-, boosting-, and hybrid-based approaches. IEEE T. Syst. Man Cy. C, 42(4): 463-484.
   
7. Greiner, R., 1992. Probabilistic hill-climbing: Theory and applications. Proceeding of the 9th Biennial Conference of the Canadian Society for Computational Studies of Intelligence (CSCSI-92), pp: 60.
   
8. Kaplan, J. and M. DeMaria, 1995. A simple empirical model for predicting the decay of tropical cyclone winds after landfall. J. Appl. Meteorol., 34(11): 2499-2512.
   CrossRef    Direct Link
9. Kaplan, J. and M. DeMaria, 2003. Large-scale characteristics of rapidly intensifying tropical cyclones in the north Atlantic basin. Weather Forecast., 18(6): 1093-1108.
   CrossRef    Direct Link
10. Kaplan, J., J.J. Cione, M. DeMaria, J. Knaff, J. Dunion et al., 2010a. 9C.4 Enhancements to the operational ships rapid intensification index. Proceeding of the 29th Conference on Hurricanes and Tropical Meteorology, Rickenbacker Causeway.
    
11. Kaplan, J., M. DeMaria and J.A. Knaff, 2010b. A revised tropical cyclone rapid intensification index for the Atlantic and eastern north pacific basins. Weather Forecast., 25(1): 220-241.
    CrossRef    Direct Link
12. Kaplan, J., C.M. Rozoff, M. DeMaria, C.R. Sampson, J.P. Kossin, C.S. Velden, J.J. Cione, J.P. Dunion, J.A. Knaff, J.A. Zhang et al., 2015. Evaluating environmental impacts on tropical cyclone rapid intensification predictability utilizing statistical models. Weather Forecast., 30(5): 1374-1396.
    CrossRef    
13. Kieper, M.E. and H. Jiang, 2012. Predicting tropical cyclone rapid intensification using the 37 GHz ring pattern identified from passive microwave measurements. Geophys. Res. Lett., 39(13).
    CrossRef    Direct Link
14. Lippsett, L., 2011. Gliders tracked potential for oil to reach the east coast. OCEANUS Mag., 48(3).
    
15. Maimon, O. and L. Rokach, 2005. Data Mining and Knowledge Discovery Handbook. Springer-Verlag, New York, Inc., Secaucus, NJ, USA.
    
16. Meisner, B.N., 2006. An overview of NHC prediction model. NOAA Technical Attachment SR/SSD 95-36.
    
17. National Hurricane Center, 2009. NHC Track and Intensity Models. pp: 18.
    Direct Link
18. Rhome, J.R., 2007. Technical summary of the national hurricane center track and intensity models. Technical Report, National Hurricane Center, 2007.
    Direct Link
19. Rozoff, C.M. and J.P. Kossin, 2011. New probabilistic forecast models for the prediction of tropical cyclone rapid intensification. Weather Forecast., 26(5): 677-689.
    CrossRef    Direct Link
20. Russell, S. and P. Norvig, 1995. Artificial Intelligence: A Modern Approach. Prentice-Hall, Egnlewood Cliffs, NJ.
    
21. Schott, T., C. Landsea, G. Hafele, J. Lorens, A. Taylor, H. Thurm, B. Ward, M. Willis and W. Zaleski, 2012. The Saffir-Simpson Hurricane Wind Scale. National Weather Services, National Hurricane Centre, National Oceanic and Atmospheric Administration (NOAA) Factsheet.
    Direct Link
22. Shay, L.K., G.J. Goni and P.G. Black, 2000. Effects of a warm oceanic feature on hurricane opal. Mon. Weather Rev., 128(5): 1366-1383.
    CrossRef    Direct Link
23. Tan, P.N., M. Steinbach and V. Kumar, 2005. Introduction to Data Mining. 1st Edn., Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA.
    
24. Torgo, L., 2010. Data Mining with R: Learning with case studies. Chapman and Hall/CRC, Taylor & Francis Group, Boca Raton, London, New York.
    Direct Link
25. Walker, S.H. and D.B. Duncan, 1967. Estimation of the probability of an event as a function of several independent variables. Biometrika, 54(1-2): 167-179.
    CrossRef    PMid:6049533    
26. Wang, B. and X. Zhou, 2008. Climate variation and prediction of rapid intensification in tropical cyclones in the western north pacific. Meteorol. Atmos. Phys., 99(1): 1-16.
    CrossRef    Direct Link

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The authors have no competing interests.

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