Tornado prediction methods are analyzed. A model, based on the superposition principle applied for different methods of data analysis has been built. For efficiency evaluation, the constructed model has been tested on real data. It is shown that the constructed tornado prediction model is more efficient than previous models.

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.