Abstract
The National Weather Service (NWS) Mesocyclone Detection Algorithms (MDA) use empirical rules to process velocity data from the Weather Surveillance Radar 1988 Doppler (WSR-88D). In this study Support Vector Machines (SVM) are applied to mesocyclone detection. Comparison with other classification methods like neural networks and radial basis function networks show that SVM are more effective in mesocyclone/tornado detection.
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Keywords
- Support Vector Machine
- False Alarm
- Radial Basis Function Network
- Perfect Score
- Training Support Vector Machine
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Trafalis, T.B., Ince, H., Richman, M.B. (2003). Tornado Detection with Support Vector Machines. In: Sloot, P.M.A., Abramson, D., Bogdanov, A.V., Gorbachev, Y.E., Dongarra, J.J., Zomaya, A.Y. (eds) Computational Science — ICCS 2003. ICCS 2003. Lecture Notes in Computer Science, vol 2660. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44864-0_30
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DOI: https://doi.org/10.1007/3-540-44864-0_30
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