Multi-Output Artificial Neural Network for Storm Surge Prediction in North Carolina

2016/09/23 by Anton Bezuglov, Bezuglov, Anton, Brian Blanton +3
Earth and Planetary Sciences · Environmental Science · #Tropical and Extratropical Cyclones Research #Meteorological Phenomena and Simulations #Hydrological Forecasting Using AI

paper · pdf · doi:10.48550/arxiv.1609.07378

Abstract

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.

Citations

• The International Best Track Archive for Climate Stewardship (IBTrACS)
• A Basin- to Channel-Scale Unstructured Grid Hurricane Storm Surge Model Applied to Southern Louisiana

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