Wave runup is one of the most critical parameters contributing to coastline flooding and
shoreline change. Many formulas have been developed to empirically predict wave runup …

This is a systematic literature review of the application of machine learning (ML) algorithms
in geosciences, with a focus on environmental monitoring applications. ML algorithms, with …

Wave runup estimation is of major interest to coastal engineers for identifying vulnerable
and safe areas in coastal regions. Recently, prediction using machine learning (ML) …

Coastal storms cause widespread damage to property, infrastructure, economic activity, and
the environment. Along open sandy coastlines, two of the primary coastal storm hazards are …

The integration of machine learning (ML) techniques in coastal engineering marks a
paradigm shift in how coastal processes are modeled and understood. While traditional …

The present study uses nine machine learning (ML) methods to predict wave runup in an
innovative and comprehensive methodology. Unlike previous investigations, which often …

Due to recent technological advancements in the field of cloud-based satellite remote
sensing, the barriers to global analysis ready dataset access and processing have lowered …

National weather forecasting agencies routinely issue a range of hazard warnings. But to
our knowledge, along sandy coastlines where storm waves and storm surge can result in …

Wave runup is a critical factor that affects coastal flooding, shoreline changes, and the
damage to coastal structures. Climate change is also expected to amplify the impact of wave …

This study provides an extensive review of over 200 journal papers focusing on Machine
Learning (ML) algorithms' use for promoting a sustainable management of the marine and …