<ul><li>Cyclone rapid intensification is the rapid increase in cyclone wind intensity, exceeding a threshold of 30 knots, within 24 hours.</li><li>Deep learning, ensemble learning, and data augmentation frameworks are evaluated to detect cyclone rapid intensification based on wind intensity and spatial coordinates.</li><li>Conventional data augmentation methods cannot replicate cyclones that undergo rapid intensification, so deep learning models are used to address the class imbalance problem.</li><li>Results show that data augmentation improves rapid intensification detection, with spatial coordinates playing a critical role in the models, paving the way for synthetic data generation in spatiotemporal data with extreme events.</li></ul>

Spatiotemporal deep learning models for detection of rapid intensification in cyclones

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