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Prediction of future evolution of solar cycle 24 using machine learning techniques

Published online by Cambridge University Press:  27 November 2018

Sumesh Gopinath  and
P. R. Prince
Sumesh Gopinath
Affiliation:
Department of Physics, University College, Thiruvananthapuram - 695034, Kerala, India email: [email protected], [email protected]
P. R. Prince
Affiliation:
Department of Physics, University College, Thiruvananthapuram - 695034, Kerala, India email: [email protected], [email protected]
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Abstract

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Forecasting the solar activity is of great importance not only for its effect on the climate of the Earth but also on the telecommunications, power lines, space missions and satellite safety. In the present work, machine learning using Artificial Neural Networks (ANNs) called Nonlinear Autoregressive Network (NAR) with Exogenous Inputs (NARX) have been applied for the prediction of future evolution of the present sunspot cycle. NARX network is able to combine the performance of ANN algorithm with nonlinear autoregressive method to handle problems such as finding dependencies among solar indices and prediction of solar cycle evolution.

Keywords

Solar cycleMachine learning
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 13 , Symposium S340: Long-term Datasets for the Understanding of Solar and Stellar Magnetic Cycles , February 2018 , pp. 317 - 318
Copyright
Copyright © International Astronomical Union 2018 

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