Predicting coronal mass ejection travel times using enhanced model-guided machine learning

TL;DR

This paper enhances physics-informed AI models for predicting coronal mass ejection travel times by integrating an extended drag-based model, improving applicability, robustness, and enabling real-time space weather forecasting.

Contribution

It introduces a generalized physics-driven AI framework incorporating the extended drag-based model, allowing for broader CME event applicability and improved robustness.

Findings

Achieves travel-time prediction accuracy comparable to state-of-the-art methods.

Demonstrates robustness under small variations in model parameters.

Proposes a multiclass classification for CME speed regimes for operational use.

Abstract

Coronal mass ejections (CMEs) are key drivers of space weather events, posing risks to both space-borne and ground-based systems. 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. We achieve travel-time prediction accuracy comparable to state-of-the-art methods. We also perform a parametric robustness analysis, highlighting the stability of our approach under small variations in the drag coefficient. Furthermore, 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.