Constructing an Efficient Machine Learning Model for Tornado Prediction

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Constructing an Efficient Machine Learning Model for Tornado Prediction

Journal Article (2020)

Author(s)

Fuad Aleskerov (National Research University Higher School of Economics (HSE University))

Sergey Demin (National Research University Higher School of Economics (HSE University))

Michael B. Richman (University of Oklahoma)

Sergey Shvydun (National Research University Higher School of Economics (HSE University))

Theodore B. Trafalis (University of Oklahoma)

Vyacheslav Yakuba (National Research University Higher School of Economics (HSE University))

Affiliation

External organisation

DOI related publication

https://doi.org/10.1142/S0219622020500261

Machine learning Data analysis Superposition principle Tornado prediction

To reference this document use:

https://resolver.tudelft.nl/uuid:fc4c1298-8729-4f57-b38b-736937215978

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Publication Year

2020

Language

English

Affiliation

External organisation

Issue number

5

Volume number

19

Pages (from-to)

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.

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