An advanced spatio-temporal convolutional recurrent neural network for storm surge predictions

In this research paper, we study the capability of artificial neural network models to emulate storm surge based on the storm track/size/intensity history, leveraging a database of synthetic storm simulations. Traditionally, computational fluid dynamics (CFD) solvers are employed to numerically solv...

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Veröffentlicht in:	Neural computing & applications Jg. 35; H. 26; S. 18971 - 18987
Hauptverfasser:	Adeli, Ehsan, Sun, Luning, Wang, Jianxun, Taflanidis, Alexandros A.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.09.2023 Springer Nature B.V
Schlagworte:	Artificial Intelligence Artificial neural networks Coders Computational Biology/Bioinformatics Computational fluid dynamics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Gaussian process Image Processing and Computer Vision Mathematical models Neural networks Nodes Original Article Partial differential equations Probability and Statistics in Computer Science Recurrent neural networks Solvers Storm surges Storms Tidal waves Time series Advanced neural networks Recurrent neural networks Storm surge prediction Convolutional neural networks
ISSN:	0941-0643, 1433-3058
Online-Zugang:	Volltext
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