Speaker
Description
Coronal mass ejections (CMEs) are significant drivers of space weather, and accurately predicting their propagation speed is crucial for mitigating their impact on Earth’s environment. In this study, we leverage machine learning techniques to model and predict CME speed at 20R utilizing data from the Coordinated Data Analysis Workshop catalog. We considered data from Solar Cycles 23 and 24, divided into their rising, maxima, decline, and minima phases, to train multivariate linear regression, Random Forest, and XGBoost machine learning models aimed at predicting CME speeds at 20R. The machine learning models use linear speed, acceleration, width, and kinetic energy as input features to estimate CME speeds at 20R. Our results indicate that Random Forest and XGBoost models significantly outperform linear regression model across all datasets, achieving high R2 values (≈0.97) and low relative errors (6%) for most phases, especially during high solar activity. Feature importance analysis identifies CME linear speed and acceleration as the dominant predictors of CME speed at 20R. This result is consistent with physical models, which describe CME propagation as being influenced primarily by initial speed and the drag force acting through acceleration or deceleration in the interplanetary medium. The trained models were applied to available events from Solar Cycle 25, to predict CME speeds at 20R. The predicted values showed very good agreement with the actual speeds reported in the CDAW catalog. This successful application demonstrates the models’ generalizability and potential for forecasting future CME dynamics. Furthermore, such data-driven predictions can complement physics-based models—such as the Drag-Based Model—by providing reliable speed estimates at specific heliocentric distances, thereby enhancing the accuracy of space weather forecasts.