  The problem where a tropical cyclone intensifies dramatically within a short
period of time is known as rapid intensification. This has been one of the
major challenges for tropical weather forecasting. Recurrent neural networks
have been promising for time series problems which makes them appropriate for
rapid intensification. In this paper, recurrent neural networks are used to
predict rapid intensification cases of tropical cyclones from the South Pacific
and South Indian Ocean regions. A class imbalanced problem is encountered which
makes it very challenging to achieve promising performance. A simple strategy
was proposed to include more positive cases for detection where the false
positive rate was slightly improved. The limitations of building an efficient
system remains due to the challenges of addressing the class imbalance problem
encountered for rapid intensification prediction. This motivates further
research in using innovative machine learning methods.
