Spatiotemporal deep learning models for detection of rapid intensification in cyclones

• URL: http://arxiv.org/abs/2506.08397v1
• Date: Tue, 10 Jun 2025 03:13:02 GMT
• Title: Spatiotemporal deep learning models for detection of rapid intensification in cyclones
• Authors: Vamshika Sutar, Amandeep Singh, Rohitash Chandra,
• Abstract summary: Rapid intensification is considered an extreme event during a cyclone, and its occurrence is relatively rare, contributing to a class imbalance in the dataset.<n>In this paper, we evaluate deep learning, ensemble and learning data augmentation frameworks to detect cyclone rapid intensification based on wind intensity and spatial coordinates.<n>Our results show that data improves the results for rapid intensification detection in cyclones, and spatial coordinates play a critical role as input features to the given models.
• Score: 3.5507325091630264
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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