Sunspot behavior forecast using neural networks approaches

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 language	English
Title of host publication	Proceedings - 2020 IEEE International Conference on Industrial Technology, ICIT 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	696-700
Number of pages	5
ISBN (Electronic)	9781728157542
DOIs	https://doi.org/10.1109/ICIT45562.2020.9067206
Publication status	Published - Feb 2020
Externally published	Yes
Event	21st IEEE International Conference on Industrial Technology, ICIT 2020 - Buenos Aires, Argentina Duration: 26 Feb 2020 → 28 Feb 2020

Publication series

Name	Proceedings of the IEEE International Conference on Industrial Technology
Volume	2020-February

Conference

Conference	21st IEEE International Conference on Industrial Technology, ICIT 2020
Country	Argentina
City	Buenos Aires
Period	26.2.20 → 28.2.20

ASJC Scopus subject areas

Computer Science Applications
Electrical and Electronic Engineering

Access to Document

10.1109/ICIT45562.2020.9067206

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Cite this

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

Sunspot behavior forecast using neural networks approaches. / Premebida, Sthefanie; Pechebovicz, Denise; Camargo, Thiago; Nazario, Henrique; Soa, Vinicios; Baroncini, Virginia; De Morais, Erikson; Siqueira, Hugo; Oliva, Diego; Martins, Marcella.

Proceedings - 2020 IEEE International Conference on Industrial Technology, ICIT 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 696-700 9067206 (Proceedings of the IEEE International Conference on Industrial Technology; Vol. 2020-February).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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., 9067206, Proceedings of the IEEE International Conference on Industrial Technology, vol. 2020-February, Institute of Electrical and Electronics Engineers Inc., pp. 696-700, 21st IEEE International Conference on Industrial Technology, ICIT 2020, Buenos Aires, Argentina, 26.2.20. https://doi.org/10.1109/ICIT45562.2020.9067206

Premebida S, Pechebovicz D, Camargo T, Nazario H, Soa V, Baroncini V et al. Sunspot behavior forecast using neural networks approaches. In Proceedings - 2020 IEEE International Conference on Industrial Technology, ICIT 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 696-700. 9067206. (Proceedings of the IEEE International Conference on Industrial Technology). https://doi.org/10.1109/ICIT45562.2020.9067206

Premebida, Sthefanie ; Pechebovicz, Denise ; Camargo, Thiago ; Nazario, Henrique ; Soa, Vinicios ; Baroncini, Virginia ; De Morais, Erikson ; Siqueira, Hugo ; Oliva, Diego ; Martins, Marcella. / Sunspot behavior forecast using neural networks approaches. Proceedings - 2020 IEEE International Conference on Industrial Technology, ICIT 2020. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 696-700 (Proceedings of the IEEE International Conference on Industrial Technology).

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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.",

author = "Sthefanie Premebida and Denise Pechebovicz and Thiago Camargo and Henrique Nazario and Vinicios Soa and Virginia Baroncini and {De Morais}, Erikson and Hugo Siqueira and Diego Oliva and Marcella Martins",

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AU - Baroncini, Virginia

AU - De Morais, Erikson

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AB - 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.

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