Towards prediction of rapid intensification in tropical cyclones with recurrent neural networks

TL;DR

This paper explores using recurrent neural networks to predict rapid intensification in tropical cyclones, addressing class imbalance challenges and highlighting the need for further research in machine learning approaches.

Contribution

It demonstrates the application of RNNs to tropical cyclone intensification prediction and proposes a simple method to mitigate class imbalance issues.

Findings

RNNs can be used for rapid intensification prediction.

Class imbalance significantly affects model performance.

Including more positive cases slightly improves false positive rate.

Abstract

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.