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The progress in Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) has significantly enhanced the technologies used for predicting solar flares. These advancements enable better analysis and interpretation of complex solar data, leading to improved accuracy in forecasting potentially disruptive solar events. Since solar flares can impact global communication networks, power systems, and pose health risks to astronauts due to elevated radiation levels, there is a pressing need for precise and timely predictive models. This paper explores various modern approaches to solar flare prediction, highlighting the contribution of AI-based tools in enhancing their accuracy and lead time. The integration of ML and DL not only refines these models but also presents challenges related to their complexity and data requirements. By assessing the strengths and weaknesses of these techniques and proposing potential improvements, this paper aims to provide an extensive review of various existing techniques and methods that have been deployed in the literature for solar flare prediction. It also presents a comparative study of various solar flare prediction models.

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Decoding the Sun Using Artificial Intelligence: An Exhaustive Review of Solar Flare Forecasting from Data Streams to Dynamic Predictions with Complex Machine Learning and Deep Learning Models

  • 会议论文

  • 发表日期：

    2026年

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  • Tatavarthi Lakshmi Chandrasena,
  • Arashdeep Kaur

摘要

The progress in Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) has significantly enhanced the technologies used for predicting solar flares. These advancements enable better analysis and interpretation of complex solar data, leading to improved accuracy in forecasting potentially disruptive solar events. Since solar flares can impact global communication networks, power systems, and pose health risks to astronauts due to elevated radiation levels, there is a pressing need for precise and timely predictive models. This paper explores various modern approaches to solar flare prediction, highlighting the contribution of AI-based tools in enhancing their accuracy and lead time. The integration of ML and DL not only refines these models but also presents challenges related to their complexity and data requirements. By assessing the strengths and weaknesses of these techniques and proposing potential improvements, this paper aims to provide an extensive review of various existing techniques and methods that have been deployed in the literature for solar flare prediction. It also presents a comparative study of various solar flare prediction models.