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Intelligent Engineering Systems through Artificial Neural Networks, Volume 16

Editor
Cihan H. Dagli
Cihan H. Dagli
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Anna L. Buczak
Anna L. Buczak
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David L. Enke
David L. Enke
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Mark Embrechts
Mark Embrechts
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Okan Ersoy
Okan Ersoy
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ISBN-10:
0791802566
No. of Pages:
1000
Publisher:
ASME Press
Publication date:
2006

In this paper, different types of machine learning classifiers, such as support vector machines (SVMs), artificial neural networks (ANNs), and linear discriminant analysis (LDA), are applied for tornado detection. All methods are used to predict which storm-scale circulations yield tornadoes based on the radar derived Mesocyclone Detection Algorithm (MDA) attributes and a month attribute. The incorporation of near-storm environment (NSE) attributes as inputs to the classifiers is investigated. The sensitivity analysis for each classifier on different ratios between tornadic and non-tornadic observations in the data sets is performed. The computational results show that SVMs have a higher performance compared to ANNs and LDA.

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