Advancing Space Weather Forecasting: A Comparative Analysis of AI Techniques for Predicting Geomagnetic Storms

Authors

1 Department of Artificial Intelligence Misr University For Science And Technology Cairo, Egypt

2 Department 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.

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