Forecasting of the Geomagnetic Activity for the Next 3 Days Utilizing Neural Networks Based on Parameters Related to Large‐Scale Structures of the Solar Corona

Abstract

Abstract With the increasing number of large constellations, it is crucial to accurately predict satellite positions and movements using upper atmosphere models driven by geomagnetic indices. Machine learning (ML) can quickly provide geomagnetic index predictions. However, previous research using ML to forecast Stream Interaction Region‐driven (SIR‐driven) geomagnetic storms primarily follows two approaches: using real‐time observations at the L1 point with historical geomagnetic indices, which lacks solar source information, or using solar images as input. To forecast SIR‐driven storms over longer horizons (3 days), a neural network model is constructed by incorporating coronal magnetic field physical parameters and the coronal hole features extracted from solar images (Pch). We first select the appropriate combination of coronal magnetic parameters for forecasting geomagnetic indices. Using the Explainable AI technique, we find that the model can learn the propagation characteristics of interactions between high‐ and low‐speed solar wind from historical coronal parameters, which relate to the physical process of SIR formation, providing a physical basis for the approach. We then introduce Pch and the historical Kp index to develop an optimal model. Based on accuracy evaluations across different activity levels and solar cycle phases, the model has proven to provide reasonable forecast results for SIR‐driven events over the next 2–3 days. The model outperforms an empirical model and two ML models in terms of event detection ability for horizons longer than one day. This study offers a new approach for 3‐day geomagnetic disturbance forecasting using a deep neural network.

Keywords

• machine learning
• geomagnetic storm forecast
• deep learning
• explainable artificial intelligence