Document Type
Article
Author ORCID Identifier
Onur Vural https://orcid.org/0009-0004-4950-7520
Shah Muhammad Hamdi https://orcid.org/0000-0002-9303-7835
Soukaina Filali Boubrahimi https://orcid.org/0000-0001-5693-6383
Journal/Book Title/Conference
Remote Sensing
Volume
17
Issue
6
Publisher
MDPI AG
Publication Date
3-18-2025
Journal Article Version
Version of Record
First Page
1
Last Page
33
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Abstract
The purpose of this study is to provide a comprehensive resource for the selection of data representations for machine learning-oriented models and components in solar flare prediction tasks. Major solar flares occurring in the solar corona and heliosphere can bring potential destructive consequences, posing significant risks to astronauts, space stations, electronics, communication systems, and numerous technological infrastructures. For this reason, the accurate detection of major flares is essential for mitigating these hazards and ensuring the safety of our technology-dependent society. In response, leveraging machine learning techniques for predicting solar flares has emerged as a significant application within the realm of data science, relying on sensor data collected from solar active region photospheric magnetic fields by space- and ground-based observatories. In this research, three distinct solar flare prediction strategies utilizing the photospheric magnetic field parameter-based multivariate time series dataset are evaluated, with a focus on data representation techniques. Specifically, we examine vector-based, time series-based, and graph-based approaches to identify the most effective data representation for capturing key characteristics of the dataset. The vector-based approach condenses multivariate time series into a compressed vector form, the time series representation leverages temporal patterns, and the graph-based method models interdependencies between magnetic field parameters. The results demonstrate that the vector representation approach exhibits exceptional robustness in predicting solar flares, consistently yielding strong and reliable classification outcomes by effectively encapsulating the intricate relationships within photospheric magnetic field data when coupled with appropriate downstream machine learning classifiers.
Recommended Citation
Vural, O.; Hamdi, S.M.; Boubrahimi, S.F. Solar Flare Prediction Using Multivariate Time Series of Photospheric Magnetic Field Parameters: A Comparative Analysis of Vector, Time Series, and Graph Data Representations. Remote Sens. 2025, 17, 1075. https://doi.org/10.3390/ rs17061075
