Advancing Space Weather Forecasting: A Comparative Analysis of AI Techniques for Predicting Geomagnetic Storms | ||||
| International Integrated Intelligent Systems | ||||
| Volume 1, Issue 2, June 2024 PDF (362.89 K) | ||||
| DOI: 10.21608/iiis.2024.357835 | ||||
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| Authors | ||||
| Shaimaa Salah1; Asmaa ElSayed1; Omar Khaled1; Mohanad Deif2; Rania Elgohary2 | ||||
| 1Department of Artificial Intelligence Misr University For Science And Technology Cairo, Egypt | ||||
| 2Department of Artificial intelligence , College of Information Technology, Misr University for Science & Technology (MUST), 6th of October City 12566 , Egypt | ||||
| Abstract | ||||
| Forecasting geomagnetic storms is crucial for mitigating their potential impacts on technology and infrastructure. This research explores the use of artificial intelligence (AI) techniques, particularly linear regression, and Long Short-Term Memory (LSTM) networks, for predicting geomagnetic storms using the OMNI dataset. The dataset, comprising various solar and interplanetary parameters, was preprocessed by scaling features and removing null values. A linear regression model achieved a Root Mean Squared Error (RMSE) of 5.95 and an R² score of 0.77. In contrast, the LSTM model, designed to capture temporal dependencies, significantly outperformed linear regression with an RMSE of 1.46 and an R² score of 0.99. These results demonstrate the potential of LSTM networks in accurately forecasting geomagnetic activity, thus providing a valuable tool for space weather prediction and the protection of critical technological systems. | ||||
| Keywords | ||||
| Geomagnetic Storms; Forecasting; NASA; Deep , learning; Machine learning; Artificial Intelligence | ||||
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