Standard
On the Possibility of Using Neural Networks for the Thunderstorm Forecasting. / Stankova, Elena ; Tokareva, Irina O. ; Dyachenko, Natalia V. .
Computational Science and Its Applications – ICCSA 2021: 21st International Conference, Cagliari, Italy, September 13–16, 2021, Proceedings, Part VIII. ed. / Osvaldo Gervasi; Beniamino Murgante; Sanjay Misra; Chiara Garau; Ivan Blečić; David Taniar; Bernady O. Apduhan; Ana Maria Rocha; Eufemia Tarantino; Carmelo Maria Torre. Springer Nature, 2021. p. 350-359 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12956 LNCS).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
Harvard
Stankova, E, Tokareva, IO & Dyachenko, NV 2021,
On the Possibility of Using Neural Networks for the Thunderstorm Forecasting. in O Gervasi, B Murgante, S Misra, C Garau, I Blečić, D Taniar, BO Apduhan, AM Rocha, E Tarantino & CM Torre (eds),
Computational Science and Its Applications – ICCSA 2021: 21st International Conference, Cagliari, Italy, September 13–16, 2021, Proceedings, Part VIII. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12956 LNCS, Springer Nature, pp. 350-359, International Conference on Computational Science and Its Applications, Кальяри, Italy,
13/09/21.
https://doi.org/10.1007/978-3-030-87010-2_25
APA
Stankova, E., Tokareva, I. O., & Dyachenko, N. V. (2021).
On the Possibility of Using Neural Networks for the Thunderstorm Forecasting. In O. Gervasi, B. Murgante, S. Misra, C. Garau, I. Blečić, D. Taniar, B. O. Apduhan, A. M. Rocha, E. Tarantino, & C. M. Torre (Eds.),
Computational Science and Its Applications – ICCSA 2021: 21st International Conference, Cagliari, Italy, September 13–16, 2021, Proceedings, Part VIII (pp. 350-359). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12956 LNCS). Springer Nature.
https://doi.org/10.1007/978-3-030-87010-2_25
Vancouver
Stankova E, Tokareva IO, Dyachenko NV.
On the Possibility of Using Neural Networks for the Thunderstorm Forecasting. In Gervasi O, Murgante B, Misra S, Garau C, Blečić I, Taniar D, Apduhan BO, Rocha AM, Tarantino E, Torre CM, editors, Computational Science and Its Applications – ICCSA 2021: 21st International Conference, Cagliari, Italy, September 13–16, 2021, Proceedings, Part VIII. Springer Nature. 2021. p. 350-359. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
https://doi.org/10.1007/978-3-030-87010-2_25
Author
Stankova, Elena ; Tokareva, Irina O. ; Dyachenko, Natalia V. . /
On the Possibility of Using Neural Networks for the Thunderstorm Forecasting. Computational Science and Its Applications – ICCSA 2021: 21st International Conference, Cagliari, Italy, September 13–16, 2021, Proceedings, Part VIII. editor / Osvaldo Gervasi ; Beniamino Murgante ; Sanjay Misra ; Chiara Garau ; Ivan Blečić ; David Taniar ; Bernady O. Apduhan ; Ana Maria Rocha ; Eufemia Tarantino ; Carmelo Maria Torre. Springer Nature, 2021. pp. 350-359 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
BibTeX
@inproceedings{39a141f66cc846d381fea374b748fb9d,
title = "On the Possibility of Using Neural Networks for the Thunderstorm Forecasting",
abstract = "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.",
keywords = "Machine learning, Neural networks, Perceptron complex, Radial basic neural network, Probabilistic neural network, Numerical model of convective cloud, Weather forecasting, Thunderstorm forecasting, REAL-TIME FORECAST, WEATHER, MODEL",
author = "Elena Stankova and Tokareva, {Irina O.} and Dyachenko, {Natalia V.}",
note = "Stankova E., Tokareva I.O., Dyachenko N.V. (2021) On the Possibility of Using Neural Networks for the Thunderstorm Forecasting. In: Gervasi O. et al. (eds) Computational Science and Its Applications – ICCSA 2021. ICCSA 2021. Lecture Notes in Computer Science, vol 12956. Springer, Cham. https://doi.org/10.1007/978-3-030-87010-2_25; null ; Conference date: 13-09-2021 Through 16-09-2021",
year = "2021",
doi = "10.1007/978-3-030-87010-2_25",
language = "English",
isbn = "978-3-030-87009-6",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Nature",
pages = "350--359",
editor = "Osvaldo Gervasi and Beniamino Murgante and Sanjay Misra and Chiara Garau and Ivan Ble{\v c}i{\'c} and David Taniar and Apduhan, {Bernady O.} and Rocha, {Ana Maria} and Eufemia Tarantino and Torre, {Carmelo Maria}",
booktitle = "Computational Science and Its Applications – ICCSA 2021",
address = "Germany",
}
RIS
TY - GEN
T1 - On the Possibility of Using Neural Networks for the Thunderstorm Forecasting
AU - Stankova, Elena
AU - Tokareva, Irina O.
AU - Dyachenko, Natalia V.
N1 - Conference code: 21
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Machine learning
KW - Neural networks
KW - Perceptron complex
KW - Radial basic neural network
KW - Probabilistic neural network
KW - Numerical model of convective cloud
KW - Weather forecasting
KW - Thunderstorm forecasting
KW - REAL-TIME FORECAST
KW - WEATHER
KW - MODEL
UR - http://www.scopus.com/inward/record.url?scp=85115684272&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/5c2f4ec1-af97-34c4-8477-ea456c05c080/
U2 - 10.1007/978-3-030-87010-2_25
DO - 10.1007/978-3-030-87010-2_25
M3 - Conference contribution
SN - 978-3-030-87009-6
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 350
EP - 359
BT - Computational Science and Its Applications – ICCSA 2021
A2 - Gervasi, Osvaldo
A2 - Murgante, Beniamino
A2 - Misra, Sanjay
A2 - Garau, Chiara
A2 - Blečić, Ivan
A2 - Taniar, David
A2 - Apduhan, Bernady O.
A2 - Rocha, Ana Maria
A2 - Tarantino, Eufemia
A2 - Torre, Carmelo Maria
PB - Springer Nature
Y2 - 13 September 2021 through 16 September 2021
ER -
