Korsos, M.B., Erdelyi, R. orcid.org/0000-0003-3439-4127, Liu, J. et al. (1 more author) (2021) Testing and validating two morphological flare predictors by logistic regression machine learning. Frontiers in Astronomy and Space Sciences, 7. 571186. ISSN 2296-987X
Abstract
Whilst the most dynamic solar active regions (ARs) are known to flare frequently, predicting the occurrence of individual flares and their magnitude, is very much a developing field with strong potentials for machine learning applications. The present work is based on a method which is developed to define numerical measures of the mixed states of ARs with opposite polarities. The method yields compelling evidence for the assumed connection between the level of mixed states of a given AR and the level of the solar eruptive probability of this AR by employing two morphological parameters: 1) the separation parameter Sl−f and 2) the sum of the horizontal magnetic gradient GS. In this work, we study the efficiency of Sl−f and GS as flare predictors on a representative sample of ARs, based on the SOHO/MDI-Debrecen Data (SDD) and the SDO/HMI - Debrecen Data (HMIDD) sunspot catalogues. In particular, we investigate about 1,000 ARs in order to test and validate the joint prediction capabilities of the two morphological parameters by applying the logistic regression machine learning method. Here, we confirm that the two parameters with their threshold values are, when applied together, good complementary predictors. Furthermore, the prediction probability of these predictor parameters is given at least 70% a day before.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 Korsós, Erdélyi, Liu and Morgan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (http://creativecommons.org/licenses/by/4.0/). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
Keywords: | morphological parameters; validation; binary logistic regression; machine learning; flare prediction |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Science (Sheffield) > School of Mathematics and Statistics (Sheffield) |
Funding Information: | Funder Grant number Science and Technology Facilities Council ST/M000826/1 The Royal Society IE161153 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 18 Jan 2021 14:12 |
Last Modified: | 09 Nov 2021 17:06 |
Status: | Published |
Publisher: | Frontiers Media |
Refereed: | Yes |
Identification Number: | 10.3389/fspas.2020.571186 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:169337 |