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Constructing an Efficient Machine Learning Model for Tornado Prediction

Author

Listed:
  • Fuad Aleskerov

    (National Research University Higher School of Economics, 20 Myasnitskaya Street, V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, 65 Profsoyuznaya Street, Moscow, Russia)

  • Sergey Demin

    (National Research University Higher School of Economics, 20 Myasnitskaya Street, V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, 65 Profsoyuznaya Street, Moscow, Russia)

  • Michael B. Richman

    (University of Oklahoma School of Meteorology, 120 David L Boren Blvd., Norman, OK, USA)

  • Sergey Shvydun

    (National Research University Higher School of Economics, 20 Myasnitskaya Street, V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, 65 Profsoyuznaya Street, Moscow, Russia)

  • 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)

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.

Suggested Citation

  • Fuad Aleskerov & Sergey Demin & Michael B. Richman & Sergey Shvydun & Theodore B. Trafalis & Vyacheslav Yakuba, 2020. "Constructing an Efficient Machine Learning Model for Tornado Prediction," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 19(05), pages 1177-1187, August.
    • Handle: RePEc:wsi:ijitdm:v:19:y:2020:i:05:n:s0219622020500261
    DOI: 10.1142/S0219622020500261
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    Cited by:

    1. Fahim Sufi & Edris Alam & Musleh Alsulami, 2022. "Automated Analysis of Australian Tropical Cyclones with Regression, Clustering and Convolutional Neural Network," Sustainability, MDPI, vol. 14(16), pages 1-23, August.
    2. Fahim Sufi & Edris Alam & Musleh Alsulami, 2022. "A New Decision Support System for Analyzing Factors of Tornado Related Deaths in Bangladesh," Sustainability, MDPI, vol. 14(10), pages 1-18, May.

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