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ASME Press Select Proceedings
Intelligent Engineering Systems through Artificial Neural Networks, Volume 16
ISBN-10:
0791802566
No. of Pages:
1000
Publisher:
ASME Press
Publication date:
2006
eBook Chapter
101 Machine Learning Classifiers for Tornado Detection: Sensitivity Analysis on Tornado Data Sets
By
Jin Park
Jin Park
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Page Count:
6
-
Published:2006
Citation
Adrianto, I, Trafalis, TB, Richman, MB, Lakshmivarahan, S, & Park, J. "Machine Learning Classifiers for Tornado Detection: Sensitivity Analysis on Tornado Data Sets." Intelligent Engineering Systems through Artificial Neural Networks, Volume 16. Ed. Dagli, CH, Buczak, AL, Enke, DL, Embrechts, M, & Ersoy, O. ASME Press, 2006.
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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...
Abstract
1. Introduction
2. Data and Analysis
3. Methodology
4. Experiments
5. Results
6. Conclusions
Acknowledgement
References
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