  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 solvers are employed to numerically solve the
storm surge governing equations that are Partial Differential Equations and are
generally very costly to simulate. 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
very expensive CFD solvers. 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, a Long short-term memory, a
variation of Recurrent Neural Network, which is enriched with Convolutional
Neural Networks. The convolutional neural network is employed to capture the
correlation of data spatially. Therefore, the temporal and spatial correlations
of data are captured by the combination of the mentioned models, the ConvLSTM
model. As the problem is a sequence to sequence time-series problem, an
encoder-decoder ConvLSTM model is designed. Some other techniques in the
process of model training are also employed to enrich the model performance.
The results show the proposed convolutional recurrent neural network
outperforms the Gaussian Process implementation for the examined synthetic
storm database.
