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

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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.

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Acknowledgements

Authors would like to thank the Army Corp of Engineers, Coastal Hydraulics Laboratory of the Engineering Research and Development Center for providing access to the storm surge data used in the illustrative case study, through the coastal hazards system (https://chs.erdc.dren.mil/).

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Authors and Affiliations

  1. Department of Civil & Environmental Engineering & Earth Sciences, University of Notre Dame, Notre Dame, IN, 46556, USA

    Ehsan Adeli & Alexandros A. Taflanidis

  2. Computational Mechanics & Scientific AI Lab, Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN, 46556, USA

    Luning Sun & Jianxun Wang

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  1. Ehsan Adeli
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  4. Alexandros A. Taflanidis
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Correspondence to Ehsan Adeli.

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Appendix: Test grids

Appendix: Test grids

In this section, the comparison of the prediction by the developed convolutional recurrent neural network and Gaussian process is shown in Figs. 9 and 10. Figure 9 shows the comparison for a grid from the early layers of the coast, and Fig. 10 shows a grid from the last layer of grids in the coast. As it is mentioned in Sect. 4, the results by convolutional recurrent neural network model are much better and more accurate than the Gaussian process.

Fig. 9
figure 9

Storm surge predictions for one grid SP in different test storms for a grid point in the coast front layers

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Fig. 10
figure 10

Storm surge predictions for one grid SP in different test storms for a grid point in the coast back layers

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Adeli, E., Sun, L., Wang, J. et al. An advanced spatio-temporal convolutional recurrent neural network for storm surge predictions. Neural Comput & Applic 35, 18971–18987 (2023). https://doi.org/10.1007/s00521-023-08719-2

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