Tornado detection with support vector machines

Authors:
Theodore B. Trafalis;Huseyin Ince;Michael B. Richman
Affiliations:
School of Industrial Engineering, University of Oklahoma, Norman, OK;School of Industrial Engineering, University of Oklahoma, Norman, OK;School of Meteorology, University of Oklahoma, Norman, OK
Venue:
ICCS'03 Proceedings of the 2003 international conference on Computational science
Year:
2003

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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.