Predicting Coronal Mass Ejection Travel Times Using Enhanced Model-Guided Machine Learning

Coronal mass ejections (CMEs) are key drivers of space weather events, posing risks to both space-borne and ground-based systems. An accurate prediction of their arrival time at Earth is critical for impact mitigation. To this end, physics-informed artificial intelligence (AI) approaches have proven more effective than purely data-driven or physics-based methods, generally offering higher accuracy and better explainability than the former and lower computational cost than the latter. In this work, we propose a generalization of the physics-driven AI framework based on the classical drag-based model (DBM) by integrating the extended version of the drag-based model (EDBM). This enhancement allows us to include in the training process CME events whose interplanetary dynamics are incompatible with those assumed by the DBM. The method is evaluated on a data set of 41 CMEs propagating in the cross-wind decelerating regime, achieving a mean absolute error of approximately 13 hr. We further perform a sensitivity analysis on the empirical drag coefficient , varied within the range 1-160, showing stable performance and moderate sensitivity, with optimal results around . These results indicate robustness of the proposed framework under physically reasonable variations of model parameters. Finally, we propose a categorization of CMEs into speed regimes defined by the EDBM using a multiclass classification model based on logistic regression, which could be implemented in near-real-time operational space weather forecasting systems. The results show that the EDBM framework broadens the applicability of forecasting models while preserving good predictive accuracy.