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The Sun constantly releases radiation and plasma into the heliosphere.Sporadically, the Sun launches solar eruptions such as flares and coronal massejections (CMEs). CMEs carry away a huge amount of mass and magnetic flux withthem. An Earth-directed CME can cause serious consequences to the human system.It can destroy power grids/pipelines, satellites, and communications.Therefore, accurately monitoring and predicting CMEs is important to minimizedamages to the human system. In this study we propose an ensemble learningapproach, named CMETNet, for predicting the arrival time of CMEs from the Sunto the Earth. We collect and integrate eruptive events from two solar cycles,#23 and #24, from 1996 to 2021 with a total of 363 geoeffective CMEs. The dataused for making predictions include CME features, solar wind parameters and CMEimages obtained from the SOHO/LASCO C2 coronagraph. Our ensemble learningframework comprises regression algorithms for numerical data analysis and aconvolutional neural network for image processing. Experimental results showthat CMETNet performs better than existing machine learning methods reported inthe literature, with a Pearson product-moment correlation coefficient of 0.83and a mean absolute error of 9.75 hours.

Ensemble Learning for CME Arrival Time Prediction