  The Sun constantly releases radiation and plasma into the heliosphere.
Sporadically, the Sun launches solar eruptions such as flares and coronal mass
ejections (CMEs). CMEs carry away a huge amount of mass and magnetic flux with
them. 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 minimize
damages to the human system. In this study we propose an ensemble learning
approach, named CMETNet, for predicting the arrival time of CMEs from the Sun
to 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 data
used for making predictions include CME features, solar wind parameters and CME
images obtained from the SOHO/LASCO C2 coronagraph. Our ensemble learning
framework comprises regression algorithms for numerical data analysis and a
convolutional neural network for image processing. Experimental results show
that CMETNet performs better than existing machine learning methods reported in
the literature, with a Pearson product-moment correlation coefficient of 0.83
and a mean absolute error of 9.75 hours.
