Spatiotemporal deep learning models for detection of rapid intensification in cyclones

TL;DR

This paper develops deep learning and data augmentation techniques to improve the detection of rapid cyclone intensification, addressing class imbalance and highlighting the importance of spatial features.

Contribution

It introduces a novel framework that uses deep learning for synthetic spatiotemporal data generation and classification of cyclone intensification events.

Findings

Data augmentation enhances detection accuracy.

Spatial coordinates are crucial features.

Synthetic data generation aids in class imbalance.

Abstract

Cyclone rapid intensification is the rapid increase in cyclone wind intensity, exceeding a threshold of 30 knots, within 24 hours. Rapid intensification is considered an extreme event during a cyclone, and its occurrence is relatively rare, contributing to a class imbalance in the dataset. A diverse array of factors influences the likelihood of a cyclone undergoing rapid intensification, further complicating the task for conventional machine learning models. In this paper, we evaluate deep learning, ensemble learning and data augmentation frameworks to detect cyclone rapid intensification based on wind intensity and spatial coordinates. We note that conventional data augmentation methods cannot be utilised for generating spatiotemporal patterns replicating cyclones that undergo rapid intensification. Therefore, our framework employs deep learning models to generate spatial coordinates and wind intensity that replicate cyclones to address the class imbalance problem of rapid intensification. We also use a deep learning model for the classification module within the data augmentation framework to differentiate between rapid and non-rapid intensification events during a cyclone. Our results show that data augmentation improves the results for rapid intensification detection in cyclones, and spatial coordinates play a critical role as input features to the given models. This paves the way for research in synthetic data generation for spatiotemporal data with extreme events.