Iranian Journal of Geophysics

Iranian Journal of Geophysics

The investigation into the impacts of solar wind-magnetosphere interactions on ionospheric electron flux using machine learning

Articles in Press

Document Type : Research Article

Authors
Mehdi Heidari 1
Seyed Majid MirRokni 2
1 Ph.D. Student, Department of Physics, Yazd University, Yazd, Iran
2 Assistant Professor, Department of Physics, Yazd University, Yazd, Iran
10.30499/ijg.2025.479953.1637
Abstract
The solar wind plays a crucial role in Earth's magnetosphere, driving nonlinear interactions that result in geomagnetic storms and energy transfer to the ionosphere. This study investigates the relationship between solar wind parameters (e.g., speed, density, and interplanetary magnetic field components) and geomagnetic indices (e.g., AL, AE) with the electron energy flux entering the ionosphere. A ten-year satellite dataset (2004–2014), covering a complete solar cycle, was analyzed. Advanced machine learning techniques, including Random Forest, Support Vector Regression, Ridge Regression, and Principal Component Analysis, were employed to identify the most influential features affecting the electron energy flux. Additionally, preprocessing steps, such as outlier detection, normalization, and feature selection, were implemented to ensure optimal model performance. The analysis revealed that the importance of parameters dynamically varies during periods of solar minimum and maximum activity, with parameters such as Bz and the AL index having distinct effects on electron energy flux. This study not only presents an innovative approach to analyzing the complex nonlinear Sun-Earth interactions but also emphasizes the need for adaptive modeling techniques that account for temporal variations. The findings of this research provide a basis for more accurate space weather and climatology predictions, as well as for improving hybrid data-driven and physical models for future studies.
Keywords
Machine learning, solar wind, ionosphere, feature selection, space weather forecasting

Subjects

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Iranian Journal of Geophysics

Articles in Press, Accepted Manuscript
Available Online from 25 February 2025