Predicting Electricity Outages Caused by Convective Storms
  We consider the problem of predicting power outages in an electrical power
grid due to hazards produced by convective storms. These storms produce extreme
weather phenomena such as intense wind, tornadoes and lightning over a small
area. In this paper, we discuss the application of state-of-the-art machine
learning techniques, such as random forest classifiers and deep neural
networks, to predict the amount of damage caused by storms. We cast this
application as a classification problem where the goal is to classify storm
cells into a finite number of classes, each corresponding to a certain amount
of expected damage. The classification method use as input features estimates
for storm cell location and movement which has to be extracted from the raw
data.
  A main challenge of this application is that the training data is heavily
imbalanced as the occurrence of extreme weather events is rare. In order to
address this issue, we applied SMOTE technique.
