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A Model for Recommending Historical Similar Events in Forecasting CME Geoeffectiveness
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DOI：10.1029/2026SW004998.png)
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Abstract
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Coronal mass ejections (CMEs) are among the key solar eruptive activities, triggering space weather disturbances. Thus, forecasting their geoeffectiveness has become a research focus. This study constructs a model to recommend similar events for forecasting the geoeffectiveness of CMEs. The input parameters are optimized via feature dimensionality reduction, while the cosine similarity algorithm, combined with logistic regression, is employed to perform four tasks: binary classification of whether a CME will reach Earth, prediction of travel time, assessment of the intensity of geomagnetic disturbances, and matching of historical similar events. Experimental results show that the model achieves an F1-score of 0.43 for binary classification, with a mean absolute error (MAE) of 13.75 hr for travel time prediction and a MAE of 1.68 for the maximum Kp value (Kp max) on the test set. The findings indicate that the model not only achieves staged multi-task forecasting for the geoeffectiveness of CMEs but also enhances the interpretability of forecasting results through similar event recommendation, providing pragmatic decision support for space weather warnings.
Keywords:
coronal mass ejection (CME)
recommendation model
multi-objective prediction
logistic regression
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