During hurricane seasons, emergency managers and other decision makers need accurate and `on-time' information on potential storm surge impacts. Fully dynamical computer models, such as the ADCIRC tide, storm surge, and wind-wave model take several hours to complete a forecast when configured at high spatial resolution. Additionally, statically meaningful ensembles of high-resolution models (needed for uncertainty estimation) cannot easily be computed in near real-time. This paper discusses an artificial neural network model for storm surge prediction in North Carolina. The network model provides fast, real-time storm surge estimates at coastal locations in North Carolina. The paper studies the performance of the neural network model vs. other models on synthetic and real hurricane data.
@article{arxiv.1609.07378,
title = {Multi-Output Artificial Neural Network for Storm Surge Prediction in North Carolina},
author = {Anton Bezuglov and Brian Blanton and Reinaldo Santiago},
journal= {arXiv preprint arXiv:1609.07378},
year = {2016}
}