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A study on precursors leading to geomagnetic storms using artificial neural network

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Abstract

Space weather prediction involves advance forecasting of the magnitude and onset time of major geomagnetic storms on Earth. In this paper, we discuss the development of an artificial neural network-based model to study the precursor leading to intense and moderate geomagnetic storms, following halo coronal mass ejection (CME) and related interplanetary (IP) events. IP inputs were considered within a 5-day time window after the commencement of storm. The artificial neural network (ANN) model training, testing and validation datasets were constructed based on 110 halo CMEs (both full and partial halo and their properties) observed during the ascending phase of the 24th solar cycle between 2009 and 2014. The geomagnetic storm occurrence rate from halo CMEs is estimated at a probability of 79%, by this model.

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Acknowledgements

This work is financially supported by ISRO, Bangalore under ISRO-SSPS to BHU. We acknowledge the authors of the LASCO/SOHO catalogue list of CMEs, available online at http://cdaw.gsfc.nasa.gov/CMElist http://cdaw.gsfc.nasa.gov/CMElist which was used for our study. We would also like to thank 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. We are also thankful to the reviewers for their suggestions to improve the quality of the manuscript.

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  1. Atmospheric Research Laboratory, Department of Physics, Banaras Hindu University, Varanasi, 221 005, India.

    GAURAV SINGH & A K SINGH

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  1. GAURAV SINGH
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  2. A K SINGH
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Correspondence to A K SINGH.

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Corresponding editor: K Krishnamoorthy

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SINGH, G., SINGH, A.K. A study on precursors leading to geomagnetic storms using artificial neural network. J Earth Syst Sci 125, 899–908 (2016). https://doi.org/10.1007/s12040-016-0702-1

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  • DOI: https://doi.org/10.1007/s12040-016-0702-1

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