The rise of machine learning (ML) has significantly advanced the field of coastal
oceanography. This review aims to examine the existing deficiencies in numerical …

Coastal bridges are vulnerable to complicated hydrodynamics induced by hostile natural
hazards, relevant research is thus required to ensure the safe operation of these critical …

Rapid and accurate prediction of peak storm surges across an extensive coastal region is
necessary to inform assessments used to design the systems that protect coastal …

Storm surge generated from low-probability high-consequence tropical cyclones is a major
flood hazard to the New York metropolitan area and its assessment requires a large number …

With the goal to facilitate evaluation and mitigation of the risks from natural hazards, the
Natural Hazards Engineering Research Infrastructure's Computational Modeling, and …

With the increasing impact of climate change and relative sea level rise, low-lying coastal
communities face growing risks from recurrent nuisance flooding and storm tides. Thus …

In recent times, there has been an increasing prevalence of surrogate models and
metamodeling techniques in approximating the responses of complex systems. These …

Storm surge and waves are responsible for a substantial portion of tropical and extratropical
cyclones-related damages. While high-fidelity numerical models have significantly …

Rapid and precise forecasting of storm surge in coastal regions is crucial for ensuring safety
of coastal communities' life and property. Yet, learning a data-driven forecasting model from …

The present study describes a novel way of a systematic and objective selection procedure
for the development of an Artificial Neural Network-based storm Surge Forecast Model (ANN …