Convolutional variational autoencoder for Northeast US coastal wind and flood hazard data augmentation

The coasts of the Northeastern United States experience wind and flood damage as a result of extratropical cyclones (such as Nor’easters). However, recorded data is limited for hazard analysis and resilience evaluation. This paper describes a method that can efficiently augment the time series of ex...

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Veröffentlicht in:	Neural computing & applications Jg. 37; H. 16; S. 9537 - 9564
Hauptverfasser:	Jia, Yiming, Sasani, Mehrdad
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.06.2025 Springer Nature B.V
Schlagworte:	Artificial Intelligence Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Cyclones Data augmentation Data Mining and Knowledge Discovery Flood damage Generative adversarial networks Goodness of fit Hazard assessment Image Processing and Computer Vision Machine learning Multivariate analysis Normal distribution Original Article Probability and Statistics in Computer Science Statistical analysis Time measurement Time series Wind damage Wind speed United States > US Data augmentation Extratropical cyclone Wind speed Flood elevation Convolutional variational autoencoder Time series
ISSN:	0941-0643, 1433-3058
Online-Zugang:	Volltext
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