Title: Stacked transfer learning for tropical cyclone intensity prediction,
Abstract: Tropical cyclone wind-intensity prediction is a challenging task considering
drastic changes climate patterns over the last few decades. In order to develop
robust prediction models, one needs to consider different characteristics of
cyclones in terms of spatial and temporal characteristics. Transfer learning
incorporates knowledge from a related source dataset to compliment a target
datasets especially in cases where there is lack or data. Stacking is a form of
ensemble learning focused for improving generalization that has been recently
used for transfer learning problems which is referred to as transfer stacking.
In this paper, we employ transfer stacking as a means of studying the effects
of cyclones whereby we evaluate if cyclones in different geographic locations
can be helpful for improving generalization performs. Moreover, we use
conventional neural networks for evaluating the effects of duration on cyclones
in prediction performance. Therefore, we develop an effective strategy that
evaluates the relationships between different types of cyclones through
transfer learning and conventional learning methods via neural networks.