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A Comparative Analysis of Machine-learning Models for Solar Flare Forecasting: Identifying High-performing Active Region Flare Indicators
Indian Inst Sci Educ & Res Kolkata, Ctr Excellence Space Sci India, Mohanpur 741246, W Bengal, India..
Indian Inst Sci Educ & Res Kolkata, Ctr Excellence Space Sci India, Mohanpur 741246, W Bengal, India.;Indian Inst Sci Educ & Res Kolkata, Dept Phys Sci, Mohanpur 741246, W Bengal, India..
Indian Inst Sci Educ & Res Kolkata, Ctr Excellence Space Sci India, Mohanpur 741246, W Bengal, India.;Indian Inst Sci Educ & Res Kolkata, Dept Phys Sci, Mohanpur 741246, W Bengal, India..
Indian Inst Sci Educ & Res Kolkata, Ctr Excellence Space Sci India, Mohanpur 741246, W Bengal, India.;Max Planck Inst Solar Syst Res, D-37077 Gottingen, Germany..
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2022 (English)In: Astrophysical Journal, ISSN 0004-637X, E-ISSN 1538-4357, Vol. 935, no 1, article id 45Article in journal (Refereed) Published
Abstract [en]

Solar flares create adverse space weather impacting space- and Earth-based technologies. However, the difficulty of forecasting flares, and by extension severe space weather, is accentuated by the lack of any unique flare trigger or a single physical pathway. Studies indicate that multiple physical properties contribute to active region flare potential, compounding the challenge. Recent developments in machine learning (ML) have enabled analysis of higher-dimensional data leading to increasingly better flare forecasting techniques. However, consensus on high-performing flare predictors remains elusive. In the most comprehensive study to date, we conduct a comparative analysis of four popular ML techniques (k nearest neighbors, logistic regression, random forest classifier, and support vector machine) by training these on magnetic parameters obtained from the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory for the entirety of solar cycle 24. We demonstrate that the logistic regression and support vector machine algorithms perform extremely well in forecasting active region flaring potential. The logistic regression algorithm returns the highest true skill score of 0.967 +/- 0.018, possibly the highest classification performance achieved with any strictly parametric study. From a comparative assessment, we establish that magnetic properties like total current helicity, total vertical current density, total unsigned flux, R_VALUE, and total absolute twist are the top-performing flare indicators. We also introduce and analyze two new performance metrics, namely, severe and clear space weather indicators. Our analysis constrains the most successful ML algorithms and identifies physical parameters that contribute most to active region flare productivity.

Place, publisher, year, edition, pages
IOP Publishing Ltd , 2022. Vol. 935, no 1, article id 45
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Astronomy, Astrophysics and Cosmology Microbiology in the medical area Radiology, Nuclear Medicine and Medical Imaging
Identifiers
URN: urn:nbn:se:kth:diva-316788DOI: 10.3847/1538-4357/ac7955ISI: 000839882800001Scopus ID: 2-s2.0-85135971542OAI: oai:DiVA.org:kth-316788DiVA, id: diva2:1691342
Note

QC 20220830

Available from: 2022-08-30 Created: 2022-08-30 Last updated: 2022-08-30Bibliographically approved

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Mitra, DhrubadityaChatterjee, SaikatBrandenburg, Axel

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Astronomy, Astrophysics and CosmologyMicrobiology in the medical areaRadiology, Nuclear Medicine and Medical Imaging

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