International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 06 | Jun 2025
p-ISSN: 2395-0072
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Influence of Extreme Solar Events on Geomagnetic Variations Using Machine Learning Shivam tiwari1, Dr. Achyut Pandey2, Dr. Laxmi Tripathi3, Dr. Yash Kumar Singh4. 2Govt. T.R.S. College Rewa (M.P.)
1,3,4Govt. Model Science College Rewa (M.P.)
------------------------------------------------------------------***---------------------------------------------------------------Abstract
Solar activity produces substantial modifications in Earth's near-space environment with extreme events such as solar flares and coronal mass ejections (CMEs) leading the charge. Solar flares and coronal mass ejections create geomagnetic storms that interrupt satellite operations and communication systems along with power infrastructure by disrupting the interplanetary magnetic field and solar wind. The research examines how extreme solar events impact geomagnetic field changes using a machine learning methodology. Using a combination of solar wind parameters, geomagnetic indices (Dst, Kp), and flare classifications, we develop predictive models to forecast geomagnetic disturbances. Long Short-Term Memory (LSTM) networks and Random Forest classifiers are applied to historical data to identify and anticipate storm-scale fluctuations linked to solar triggers. The results show that machine learning methods can effectively capture complex, nonlinear interactions in the solar-terrestrial system and provide meaningful forecasts of geomagnetic responses. This work contributes to space weather research by demonstrating how artificial intelligence can enhance early-warning systems for solar-induced geomagnetic activity.
Keywords: Extreme solar events, geomagnetic storms, machine learning, solar wind, space weather forecasting
1. Introduction The geomagnetic field of the Earth, a vital defense against detrimental cosmic radiation and solar particles, is constantly molded and disturbed by solar action. Of all the numerous causes of geomagnetic variations, extreme solar flares and CMEs are the most severe external causes of geomagnetic disturbances. These events release huge energies and charged particles into the heliosphere, usually leading to geomagnetic storms when they interact with the Earth's magnetosphere [1]. The heightened frequency and magnitude of these solar phenomena, especially during phases of solar maximum, necessitate the creation of good models capable of predicting their terrestrial impact. Historically, the study of solar-driven geomagnetic perturbations has utilized statistical correlations of solar wind variables and geomagnetic indices. Although these approaches have been highly effective in yielding key insights, they tend to fail to account for the nonlinear and temporally dependent characteristics of the solar-terrestrial system. The introduction of highresolution observational information and improved computer algorithms has made machine learning a potential new avenue for space weather modeling. These algorithms excel at identifying complex patterns in large datasets, enabling predictive capabilities that extend beyond the reach of traditional physics-based models [2]. This research seeks to bridge the gap between space weather observation and actionable prediction by employing machine learning methods to assess and forecast geomagnetic variations driven by extreme solar events. By integrating solar wind parameters, X-ray flare classifications, and geomagnetic indices, we aim to construct a robust model that can learn from past events and improve short-term forecasts. Such an approach not only deepens our understanding of solar-geophysical interactions but also enhances the resilience of critical technological infrastructure vulnerable to space weather impacts.
2. Literature Review Over the past decades, numerous studies have explored the interactions between solar activity and geomagnetic field behavior, particularly focusing on the impacts of solar flares and CMEs. The Dst and Kp indices, which quantify geomagnetic storm intensity and variability, have been central to these investigations. Burton et al. [3] established early empirical models linking
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