Publication:
Recognition of Geomagnetic Storm Based on Neural Network Model Estimates of Dst Indices

Дата
2022
Авторы
Belov, A. V.
Gvishiani, A. D.
Getmanov, V. G.
Sololoviev, A. A.
Kovylyaeva, A. A.
Yashin, I. I.
Journal Title
Journal ISSN
Volume Title
Издатель
Научные группы
Организационные подразделения
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Организационная единица
Институт ядерной физики и технологий
Цель ИЯФиТ и стратегия развития - создание и развитие научно-образовательного центра мирового уровня в области ядерной физики и технологий, радиационного материаловедения, физики элементарных частиц, астрофизики и космофизики.
Выпуск журнала
Аннотация
© 2022, Pleiades Publishing, Ltd.Abstract: A method for recognizing geomagnetic storms based on the neural network (NN) model estimates of Dst indices (disturbance storm time) is proposed; observations from the URAGAN muon hodoscope (MH) and neutron monitors are used. A convolutional NN is applied. A decision-making rule for recognition is implemented. Estimates of the probabilistic characteristics of the recognition of geomagnetic storms are formed. An experimental study of the method confirms its effectiveness. It is shown that joint observations of the hodoscope-monitor system, in comparison with separate observations, increase the probability of correctly recognizing geomagnetic storms.
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Цитирование
Recognition of Geomagnetic Storm Based on Neural Network Model Estimates of Dst Indices / Belov, A.V. [et al.] // Journal of Computer and Systems Sciences International. - 2022. - 61. - № 1. - P. 54-64. - 10.1134/S106423072201004X
URI
https://www.doi.org/10.1134/S106423072201004X
 https://www.scopus.com/record/display.uri?eid=2-s2.0-85125252931&origin=resultslist
 http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=WOS_CPL&DestLinkType=FullRecord&UT=WOS:000755881800007
 https://openrepository.mephi.ru/handle/123456789/28783
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