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Eng. Proc., 2025, ICARS 2025

The 1st International Conference on Advanced Remote Sensing – Shaping Sustainable Global Landscapes (ICARS 2025)

Barcelona, Spain | 26–28 March 2025

Volume Editors:
Fabio Tosti, University of West London, UK
Andrea Benedetto, University Roma Tre, Italy
Nikolaos Michailidis, Aristotle University of Thessaloniki, Greece
Luis Ángel Ruiz, Universitat Politècnica de València (UPV), Spain

Number of Papers: 1

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Cover Story (view full-size image): The 1st International Conference on Advanced Remote Sensing—Shaping Sustainable Global Landscapes (ICARS 2025) was held in Barcelona, Spain, from March 26 to 28, 2025. It focused on the ways in [...] Read more.

10 pages, 2671 KiB

Open AccessProceeding Paper

Enhancing Solar Radiation Storm Forecasting with Machine Learning and Physics Models at Korea Space Weather Center

by Ji-Hoon Ha, Jae-Hyung Lee, JaeHun Kim, Jong-Yeon Yun, Sang Cheol Han and Wonhyeong Yi

Eng. Proc. 2025, 94(1), 1; https://doi.org/10.3390/engproc2025094001 - 5 May 2025

Viewed by 140

Abstract

Solar radiation storms, caused by high-energy solar energetic particles (SEPs) released during solar flares or coronal mass ejections (CMEs), have a substantial impact on the Earth’s environment. These storms can disrupt satellite operations, interfere with high-frequency (HF) communications, and increase the radiation exposure of high-altitude flights. To reduce these effects, the Korea Space Weather Center (KSWC) monitors and forecasts solar radiation storms using satellite data and predictive models. This paper introduces the space weather forecasting methods employed by the KSWC and the analysis approach for satellite data from GOES, SDO, the LASCO coronagraph, and STEREO. We introduce a predictive model for solar radiation storms, which is composed of two key components: (1) a machine learning model, which is trained using solar flare and CME data obtained from satellite observations, and (2) a physics-based model that incorporates the mechanisms of SEP generation through CMEs approaching the Earth. The machine learning model primarily forecasts the peak intensity of solar radiation storms based on real-time solar activity data, while the physics-informed model enhances the interpretability and understanding of the machine learning model’s predictions. The effectiveness and operability of this approach have been tested at the KSWC. Full article

(This article belongs to the Proceedings of The 1st International Conference on Advanced Remote Sensing – Shaping Sustainable Global Landscapes (ICARS 2025))

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