Tornado detection with support vector machines
Computational Science—ICCS 2003: International Conference, Melbourne …, 2003•Springer
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.
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.
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.
Springer
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