This study investigates the super intense geomagnetic storm of 10–11 May 2024, during which the Dst index reached −412 nT, marking the most severe event of the last two decades. An artificial neural network (ANN) model was developed to estimate the geomagnetic storm indices Dst, Kp, and ap using hourly solar wind parameters (Bz, E, P, N, and V) obtained from the OMNI database. The model successfully reproduced the rapid and nonlinear variations observed during the main phase of the storm. The correlation coefficients (R) between observed and estimated values were 99.5%, 98.8%, and 99.1% for Dst, Kp, and ap, respectively. The corresponding mean square error (RMSE) values were 5.9 nT for Dst, 4.2 for Kp, and 2.1 nT for ap. Despite the extreme geomagnetic disturbance conditions, the ANN architecture maintained high estimative stability and accuracy, particularly during the sharp Dst decrease associated with southward Bz excursions. These results demonstrate that ANN-based approaches can effectively model the nonlinear dynamics of superstorms and provide a reliable complementary tool for forecasting extreme geomagnetic events.
BULBUL et al. (Mon,) studied this question.