Abstract Artificial Neural Networks (ANNs) were firstly used to model ocean engineering
problems in the decade of 1990s. Since then, this soft-modelling technique has proved …

Compound extremes pose immense challenges and hazards to communities, and this is
particularly true for compound hydrometeorological extremes associated with deadly floods …

To improve coastal adaptation and management, it is critical to better understand and
predict the characteristics of sea levels. Here, we explore the capabilities of artificial …

Abstract Artificial Neural Networks (ANNs) are becoming more popular to model ocean
engineering problems. With the development of Artificial Intelligence, data-driven models …

Coastal flood damage is primarily the result of extreme sea levels. Climate change is
expected to drive an increase in these extremes. While proper estimation of changes in …

The rising sea levels due to climate change are a significant concern, particularly for
vulnerable, low-lying coastal regions like the Gulf of Guinea (GoG). To effectively address …

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

Hurricane storm surge represents a significant threat to coastal communities around the
world. Here, we use artificial neural network (ANN) models to predict storm surge levels …

As an area frequently suffering from storm surge, the Yangtze River Estuary in the East
China Sea requires fast and accurate prediction of water level for disaster prevention and …

Statistical models are an alternative to numerical models for reconstructing storm surges at a
low computational cost. These models directly link surges to metocean variables, ie …