Constructing an Efficient Machine Learning Model for Tornado Prediction

Fuad Aleskerov, Sergey Demin (), Michael B. Richman (), Sergey Shvydun, Theodore B. Trafalis () and Vyacheslav Yakuba ()
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Michael B. Richman: University of Oklahoma School of Meteorology, 120 David L Boren Blvd., Norman, OK, USA
Theodore B. Trafalis: University of Oklahoma School of Industrial and Systems Engineering and School of Meteorology, 202 W Boyd Street, Norman, OK, USA
Vyacheslav Yakuba: V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, 65 Profsoyuznaya Street, National Research University Higher School of Economics, 20 Myasnitskaya Street Moscow, Russia

International Journal of Information Technology & Decision Making (IJITDM), 2020, vol. 19, issue 05, 1177-1187

Abstract: Tornado prediction variables are analyzed using machine learning and decision analysis techniques. A model based on several choice procedures and the superposition principle is applied for different methods of data analysis. The constructed model has been tested on a database of tornadic events. It is shown that the tornado prediction model developed herein is more efficient than a previous set of machine learning models, opening the way to more accurate decisions.

Keywords: Machine learning; tornado prediction; superposition principle; data analysis (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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DOI: 10.1142/S0219622020500261

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