  The arrival time prediction of Coronal mass ejections (CMEs) is an area of
active research. Many methods with varying levels of complexity have been
developed to predict CME arrival. However, the mean absolute error (MAE) of
predictions remains above 12 hours, even with the increasing complexity of
methods. In this work we develop a new method for CME arrival time prediction
that uses magnetohydrodynamic simulations involving data-constrained
flux-rope-based CMEs, which are introduced in a data-driven solar wind
background. We found that, for 6 CMEs studied in this work, the MAE in arrival
time was ~8 hours. We further improved our arrival time predictions by using
ensemble modeling and comparing the ensemble solutions with STEREO-A&B
heliospheric imager data. This was done by using our simulations to create
synthetic J-maps. A machine learning (ML) method called the lasso regression
was used for this comparison. Using this approach, we could reduce the MAE to
~4 hours. Another ML method based on the neural networks (NNs) made it possible
to reduce the MAE to ~5 hours for the cases when HI data from both STEREO-A&B
were available. NNs are capable of providing similar MAE when only the STEREO-A
data is used. Our methods also resulted in very encouraging values of standard
deviation (precision) of arrival time. The methods discussed in this paper
demonstrate significant improvements in the CME arrival time predictions. Our
work highlights the importance of using ML techniques in combination with
data-constrained magnetohydrodynamic modeling to improve space weather
predictions.
