Implementation of an artificial neural network for storm surge forecasting

Coupled ocean-atmosphere dynamical models provide accurate measures of storm surge but remain too computationally expensive to run for real-time forecasting purposes. Therefore, it is common to utilize a parametric vortex model, implemented within a hydrodynamic model, which decreases computational time at the expense of forecast accuracy.

Recently, data-driven neural networks are being implemented as an alternative due to their combined efficiency and high accuracy. This work seeks to examine how an artificial neural network (ANN) can be used to make accurate storm surge predictions, and explores the added value of using a recurrent neural network (RNN). In particular, it is concerned with determining the parameters needed to successfully implement a neural network model for the Mid-Atlantic Bight region.