Spatiotemporal deep learning models for detection of rapid intensification in cyclones
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 17 of its 111 references that are in the map. First posted on arXiv 10 Jun 2025.
Builds on
17 of 111 references.
References · 17
- 2017Generative Adversarial Networks: An Overviewcited by 590
- 2017A systematic study of the class imbalance problem in convolutional neural networkscited by 513
- 2016NIPS 2016 Tutorial: Generative Adversarial Networkscited by 285
- 2018Generative Adversarial Network in Medical Imaging: A Reviewcited by 257
- 2020Time Series Data Augmentation for Deep Learning: A Surveycited by 226
- 2020A Review on Generative Adversarial Networks: Algorithms, Theory, and Applicationscited by 209
- 2019Explainable Machine Learning for Scientific Insights and Discoveriescited by 166
- 2021Evaluation of deep learning models for multi-step ahead time series predictioncited by 25
- 2023A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluationcited by 25
- 2021SMOTified-GAN for class imbalanced pattern classification problemscited by 20
- 2018Machine Learning for Spatiotemporal Sequence Forecasting: A Surveycited by 19
- 2020A Survey On Anti-Spoofing Methods For Face Recognition with RGB Cameras of Generic Consumer Devicescited by 18
5 more
- 2014Learning Longer Memory in Recurrent Neural Networkscited by 12
- 2021Bayesian graph convolutional neural networks via tempered MCMCcited by 9
- 2017Co-evolutionary multi-task learning for dynamic time series predictioncited by 3
- 2017Towards prediction of rapid intensification in tropical cyclones with recurrent neural networkscited by 1
- 2019Cyclone intensity estimate with context-aware cyclegancited by 1
Built on by
Citing papers per year: 2025: 1.
1 paper.