Modeling Next 3-day Kp Index Forecasting with Neural Networks and Exploring the Application of Explainable AI

The current operational needs of space weather forecasting strongly require accurate predictions of the future 3-day Kp index. Such forecasts involve a multitude of predictors, including physical parameters observed at the Earth-Sun L1 point and historical characteristics of the Kp index. Therefore, previous research primarily relied on statistical or empirical methods for prediction. However, the complex coupling of multiple parameters during geomagnetic storm events has made it challenging to quantify the contributions of various predictors to Kp index forecasting over a 3-day timescale, hindering further improvements in forecast accuracy. This study builds a 3-day Kp index forecasting model based on neural network modeling and utilizes explainable AI (Artificial Intelligence) algorithm, specifically the integrated gradient algorithm, to quantify the contributions of individual predictor. The research results indicate that the southward interplanetary magnetic field contributes significantly to Kp index prediction, accounting for 37.15% of all factors, making it the primary contributor. Following this, solar wind speed contributes 15.73%, underscoring the model's ability to capture parameters aligned with physical characteristics as the primary predictive factors during training. The contribution of historical characteristics of Kp index (recurrence characteristics) gradually increases with the forecasting horizon and reaches 68.06% at a lead time of 3-day. This substantiates the strong predictive capabilities of the AI model in forecasting geomagnetic storm events induced by high-speed solar wind streams originating from coronal holes. Furthermore, this study conducts contribution analysis on two significant geomagnetic storm events that occurred in 2015 and 2017. It reveals that the predominant predictors contributing to each event differ. This underscores the model's capability to accurately capture the complex coupling of multiple parameters in geomagnetic storm forecasting. In conclusion, this research demonstrates that employing explainable AI algorithms can help quantify the contributions of various predictive factors to Kp index forecasting to some extent. This has the potential to enhance further research and improvements in 3-day Kp index AI forecasting models.