Speaker
Description
Geomagnetic storms, characterized by sudden disturbances in Earth's magnetic field, pose significant risks to technological systems and human activities machine learning (ML). Accurate forecasting of geomagnetic storm levels moderate, intense, and super critical for mitigating these impacts. This study assesses the performance of techniques in predicting geomagnetic indices, specifically the SYM-H index, which provides high-resolution insights into storm intensity. Solar wind parameters such as velocity, proton density, temperature, flow pressure, electric field, and magnetic field were utilized as input features. Data preprocessing included interpolation and smoothing to ensure compatibility with raw datasets. Models were evaluated using metrics like Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Cross -correlation coefficient (R). Results demonstrate that ML approaches effectively capture geomagnetic storm dynamics across all levels, with notable performance improvements in predicting Super storms. This work highlights the potential of advanced ML techniques in enhancing space weather forecasting.
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