Sunspot behavior forecast using neural networks approaches

Sthefanie Premebida, Denise Pechebovicz, Thiago Camargo, Henrique Nazario, Vinicios Soa, Virginia Baroncini, Erikson De Morais, Hugo Siqueira, Diego Oliva, Marcella Martins

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The study of solar activity is of great interest for the recognition of its influence on the earth. A great step in astronomy is the prediction of solar activity, allowing better preparation for study and recognition of future solar and terrestrial events. In our research we used Neural Networks models to predict sunspot numbers based on solar activity recorded between 1818 and 2019. Solar activity data were taken from the Solar Influences Data analysis Center (SIDC) website and Sunspot Index and Long-term Solar Observations (SILSO). Results show a high potential of this processing that become a competitive approach for the sunspots prediction.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE International Conference on Industrial Technology, ICIT 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages696-700
Number of pages5
ISBN (Electronic)9781728157542
DOIs
Publication statusPublished - Feb 2020
Externally publishedYes
Event21st IEEE International Conference on Industrial Technology, ICIT 2020 - Buenos Aires, Argentina
Duration: 26 Feb 202028 Feb 2020

Publication series

NameProceedings of the IEEE International Conference on Industrial Technology
Volume2020-February

Conference

Conference21st IEEE International Conference on Industrial Technology, ICIT 2020
CountryArgentina
CityBuenos Aires
Period26.2.2028.2.20

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering

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    Premebida, S., Pechebovicz, D., Camargo, T., Nazario, H., Soa, V., Baroncini, V., De Morais, E., Siqueira, H., Oliva, D., & Martins, M. (2020). Sunspot behavior forecast using neural networks approaches. In Proceedings - 2020 IEEE International Conference on Industrial Technology, ICIT 2020 (pp. 696-700). [9067206] (Proceedings of the IEEE International Conference on Industrial Technology; Vol. 2020-February). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICIT45562.2020.9067206