Prediction of magnetic activities for Solar Cycle 25 using neural networks

Abstract. Recent advancements in artificial intelligence research have shown promising results in addressing scientific and operational challenges related to Space Weather. The abundance of historical solar wind data collected near Earth presents an opportunity to leverage modern scientific methodologies that integrate large datasets and computational modeling. In this study, we analyzed multivariate solar wind data spanning the twenty-third to twenty-fifth solar cycles to develop a predictive model for geomagnetic storms. Our improved long-short-term memory recurrent neural network model with an attention mechanism demonstrated accurate predictions of moderate events between 2023 and 2025, outperforming international reference models. We also evaluated the model's performance in predicting the intense geomagnetic storm of May 2024, which saw a significant Dst index amplitude variation exceeding 400 nT. This research contributes to the advancement of early warning systems, risk mitigation strategies, and offers a new approach to analyzing geomagnetic storms morphology.