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Prediction of intensity of moderate and intense geomagnetic storms using artificial neural network during two complete solar cycles 23 and 24

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Abstract

This work aims to predict moderate, intense, and super geomagnetic storms during the two recent solar cycles 23 and 24 encompassing the period 1996–2018 using an artificial neural network (ANN). Optimization of the neural network includes a choice of activation function, training function, learning function, hidden layers, hidden neurons, learning rate, and momentum constant. The results obtained by the present study show the ability of the ANN model to produce an accurate estimate of the probability appearance of moderate and intense storms of about 88.9%.

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Acknowledgements

Acknowledgment has to be given to authors of the LASCO/SOHO catalog list of CMEs, available online at http://cdaw.gsfc.nasa.gov/CMElist which was used for the study. The work was also supported by the National Aeronautics and Space Administration for providing us the necessary data of the most significant flare, available online at http://hesperia.gsfc.nasa.gov/goes/goes event listings/goes Xray event list 2014.txt.

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  1. Department of Mechanical Engineering, School of Engineering, Shiv Nadar University, Tehsil Dadri, Gautam Buddha Nagar, Uttar Pradesh, 201314, India

    Pratyush Kumar Singh

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  1. Pratyush Kumar Singh
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Correspondence to Pratyush Kumar Singh.

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Singh, P.K. Prediction of intensity of moderate and intense geomagnetic storms using artificial neural network during two complete solar cycles 23 and 24. Indian J Phys 96, 2235–2242 (2022). https://doi.org/10.1007/s12648-021-02192-0

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  • DOI: https://doi.org/10.1007/s12648-021-02192-0

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