  We present a new model for the probability that the Disturbance storm time
(Dst) index exceeds -100 nT, with a lead time between 1 and 3 days. $Dst$
provides essential information about the strength of the ring current around
the Earth caused by the protons and electrons from the solar wind, and it is
routinely used as a proxy for geomagnetic storms. The model is developed using
an ensemble of Convolutional Neural Networks (CNNs) that are trained using SoHO
images (MDI, EIT and LASCO). The relationship between the SoHO images and the
solar wind has been investigated by many researchers, but these studies have
not explicitly considered using SoHO images to predict the $Dst$ index.
  This work presents a novel methodology to train the individual models and to
learn the optimal ensemble weights iteratively, by using a customized
class-balanced mean square error (CB-MSE) loss function tied to a least-squares
(LS) based ensemble.
  The proposed model can predict the probability that Dst<-100 nT 24 hours
ahead with a True Skill Statistic (TSS) of 0.62 and Matthews Correlation
Coefficient (MCC) of 0.37. The weighted TSS and MCC from Guastavino et al.
(2021) is 0.68 and 0.47, respectively. An additional validation during
non-Earth-directed CME periods is also conducted which yields a good TSS and
MCC score.
