On the Possibility of Using Neural Networks for the Thunderstorm Forecasting

Elena Stankova, Irina O. Tokareva, Natalia V. Dyachenko

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review


The paper explores the possibility of forecasting such dangerous meteorological phenomena as a thunderstorm by applying five types of neural network to the output data of a hydrodynamic model that simulates dynamic and microphysical processes in convective clouds. The ideas and the result delivered in [1] are developed and supplemented by the classification error calculations and by consideration of radial basic and probabilistic neural networks. The results show that forecast accuracy of all five networks reaches values of 90%. However, the radial basis function has the advantages of the highest accuracy along with the smallest classification error. Its simple structure and short training time make this type of neuralnetwork the best one in view of accuracy versus productivity relation.
Original languageEnglish
Title of host publicationComputational Science and Its Applications – ICCSA 2021
Subtitle of host publication21st International Conference, Cagliari, Italy, September 13–16, 2021, Proceedings, Part VIII
PublisherSpringer Nature
ISBN (Electronic)978-3-030-87010-2
ISBN (Print)978-3-030-87009-6
StatePublished - 2021
EventInternational Conference on Computational Science and Its Applications - Кальяри, Italy
Duration: 13 Sep 202116 Sep 2021
Conference number: 21

Publication series

NameLecture Notes in Computer Science


ConferenceInternational Conference on Computational Science and Its Applications
Abbreviated titleICCSA


  • Machine learning
  • Neural networks
  • Perceptron complex
  • Radial basic neural network
  • Probabilistic neural network
  • Numerical model of convective cloud
  • Weather forecasting
  • Thunderstorm forecasting


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