Abstract
A rule-based Support Vector Machine (SVM) classifier is applied to tornado prediction. Twenty rules based on the National Severe Storms Laboratory’s mesoscale detection algorithm are used along with SVM to develop a hybrid forecast system for the discrimination of tornadic from non-tornadic events. The use of the Weather Surveillance Radar 1998 Doppler data, with continuous data streaming in every six minutes, presents a source for a dynamic data driven application system. Scientific inquiries based on these data are useful for dynamic data driven application systems (DDDAS). Sensitivity analysis is performed by changing the threshold values of the rules. Numerical results show that the optimal hybrid model outperforms the direct application of SVM by 12.7 percent.
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Keywords
- Support Vector Machine
- Misclassification Error
- Circulation Detection
- Tornado Warning
- Average Support Vector Machine
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Mitchell, T.M.: Machine Learning. McGraw-Hill, New York (1997)
Fung, G.M., Mangasarian, O.L., Shavlik, J.W.: Knowledge-based Support Vector Machines Classifiers, Data Mining Institute. Technical Report 01-09. Computer Sciences Department, University of Wisconsin (2001)
Fung, G.M., Mangasarian, O.L., Shavlik, J.W.: Knowledge-based Nonlinear Kernel Classifiers. Data Mining Institute Technical Report 03-02. Computer Sciences Department, University of Wisconsin (2003)
Trafalis, T.B., Santosa, B., Richman, M.B.: Tornado Detection with Kernel-based Methods. In: Dagli, C.H., Buczak, A.L., Ghosh, J., Embrechts, M., Ersoy, O. (eds.) Intelligent Engineering Systems Through Artificial Neural Networks, vol. 13, pp. 677–682. ASME Press (2003)
Trafalis, T.B., Santosa, B., Richman, M.B.: Tornado Detection with Kernel-Based Classifiers From WSR-D88 Radar. In: Darema, F. (ed.) Dynamic Data Driven Application Systems, Kluwer, Dordrecht (2004) (submitted)
Haykin, S.: Neural Networks: A Comprehensive foundation, 2nd edn. Prentice-Hall, Upper Saddle River (1999)
Schölkopf, B., Smola, A.: Learning with Kernels. MIT Press, Cambridge (2002)
Junshui, M., Zhao, Y., Ahalt, S.: OSU SVM Classifier Matlab Toolbox. Available at http://eewww.eng.ohio-state.edu/~maj/osu_SVM/
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© 2004 Springer-Verlag Berlin Heidelberg
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Trafalis, T.B., Santosa, B., Richman, M.B. (2004). Rule-Based Support Vector Machine Classifiers Applied to Tornado Prediction. In: Bubak, M., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds) Computational Science - ICCS 2004. ICCS 2004. Lecture Notes in Computer Science, vol 3038. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24688-6_88
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DOI: https://doi.org/10.1007/978-3-540-24688-6_88
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