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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Vydáno v:	Neural computing & applications Ročník 35; číslo 26; s. 18971 - 18987
Hlavní autoři:	Adeli, Ehsan, Sun, Luning, Wang, Jianxun, Taflanidis, Alexandros A.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Springer London 01.09.2023 Springer Nature B.V
Témata:	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
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Abstract	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 solve the storm surge governing equations that correspond to expensive to evaluate partial differential equations (PDE). This study presents a neural network model that can predict storm surge, informed by a database of synthetic storm simulations. This model can serve as a fast and affordable emulator for the expensive CFD solvers creating the original database. The neural network model is trained with the storm track parameters used to drive the CFD solvers, and the output of the model is the time-series evolution of the predicted storm surge across multiple nodes within the spatial domain of interest. Once the model is trained, it can be deployed for further predictions based on new storm track inputs. The developed neural network model is a time-series model, composed of a long short-term memory (LSTM), a variation of recurrent neural network (RNN), further enriched with convolutional neural networks (CNNs). The convolutional neural network is employed to capture the correlation of data spatially (across the aforementioned nodes). Therefore, the temporal and spatial correlations of data are captured by the combination of the mentioned models, representing the ConvLSTM model. As the problem is a sequence to sequence time-series problem, an encoder–decoder ConvLSTM model is designed. Furthermore, the performance of the developed convolutional recurrent neural network model is improved by residual connection networks. Additional techniques are employed in the process of model training to enrich the model performance that the model can learn from the data in a more effective way. The performance of the developed model is compared with the results provided by a Gaussian process (GP) implementation, representing a state-of-the-art alternative for establishing time-series emulation of storm surge predictions. The results show that the proposed convolutional recurrent neural network outperforms the GP implementation for the examined synthetic storm database.
AbstractList	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 solve the storm surge governing equations that correspond to expensive to evaluate partial differential equations (PDE). This study presents a neural network model that can predict storm surge, informed by a database of synthetic storm simulations. This model can serve as a fast and affordable emulator for the expensive CFD solvers creating the original database. The neural network model is trained with the storm track parameters used to drive the CFD solvers, and the output of the model is the time-series evolution of the predicted storm surge across multiple nodes within the spatial domain of interest. Once the model is trained, it can be deployed for further predictions based on new storm track inputs. The developed neural network model is a time-series model, composed of a long short-term memory (LSTM), a variation of recurrent neural network (RNN), further enriched with convolutional neural networks (CNNs). The convolutional neural network is employed to capture the correlation of data spatially (across the aforementioned nodes). Therefore, the temporal and spatial correlations of data are captured by the combination of the mentioned models, representing the ConvLSTM model. As the problem is a sequence to sequence time-series problem, an encoder–decoder ConvLSTM model is designed. Furthermore, the performance of the developed convolutional recurrent neural network model is improved by residual connection networks. Additional techniques are employed in the process of model training to enrich the model performance that the model can learn from the data in a more effective way. The performance of the developed model is compared with the results provided by a Gaussian process (GP) implementation, representing a state-of-the-art alternative for establishing time-series emulation of storm surge predictions. The results show that the proposed convolutional recurrent neural network outperforms the GP implementation for the examined synthetic storm database.
Author	Sun, Luning Wang, Jianxun Taflanidis, Alexandros A. Adeli, Ehsan
Author_xml	– sequence: 1 givenname: Ehsan surname: Adeli fullname: Adeli, Ehsan email: eadeli@nd.edu organization: Department of Civil & Environmental Engineering & Earth Sciences, University of Notre Dame – sequence: 2 givenname: Luning surname: Sun fullname: Sun, Luning organization: Computational Mechanics & Scientific AI Lab, Department of Aerospace and Mechanical Engineering, University of Notre Dame – sequence: 3 givenname: Jianxun surname: Wang fullname: Wang, Jianxun organization: Computational Mechanics & Scientific AI Lab, Department of Aerospace and Mechanical Engineering, University of Notre Dame – sequence: 4 givenname: Alexandros A. surname: Taflanidis fullname: Taflanidis, Alexandros A. organization: Department of Civil & Environmental Engineering & Earth Sciences, University of Notre Dame
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SubjectTerms	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
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Title	An advanced spatio-temporal convolutional recurrent neural network for storm surge predictions
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