A review on storm surge modeling and predictions in coastal waters

Abstract

Storm surges are among the most destructive coastal hazards, and accurate prediction is vital for disaster preparedness. This paper reviews storm surge modeling/prediction methods and applications from early two-dimensional hydrodynamic models such as the Sea, Lake, and Overland Surges from Hurricanes (SLOSH) and the Advanced Circulation Model (ADCIRC) to fully coupled atmosphere-wave-ocean models, which represent key interactions among winds, waves, and circulation. To resolve dynamics in complex coastal settings, the fully coupled models substantially improve storm surge modeling/prediction accuracy. More recently, artificial intelligence (AI) has introduced a new generation of methods: machine learning and deep learning surrogates offer rapid emulation of physics-based models, and sequence models provide efficient time-series forecasting. The newly developed AI method of the physics-informed neural networks (PINN) that combine data-driven flexibility with hydrodynamic equations may not only provide fast predictions but also reveal more dynamical mechanisms. These developments highlight clear evolutions of storm surge modeling and prediction from simplified operational models toward hybrid physics-based AI frameworks, aimed at delivering faster, more accurate, and more reliable storm surge predictions.

Keywords

storm surge / numerical modelling / coupled model / deep learning

References

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