A new scientific publication titled “Toward an AI-enhanced hydro-morphodynamic model for nature-based solutions in coastal erosion mitigation”, developed under the REST-COAST project, has been published in the journal Applied Ocean Research.

The study presents a Convolutional Neural Network (CNN) - based model that can accurately predict coastal erosion and sediment changes. The model uses key information about wave energy and water movement, which strongly influence how sediment is eroded or deposited. By focusing on these important factors, the model can be further developed to test different scenarios and support a wider range of coastal protection and restoration applications. 

The strength of this approach lies in its ability to quickly test different coastal restoration scenarios and support Nature-based Solutions for erosion mitigation. By reducing the need for complex and time-consuming simulations, the model can be applied to different coastal regions and conditions.

This allows researchers and decision-makers to explore and optimise restoration strategies more efficiently. As the model continues to develop and integrate real-world data, it has strong potential to support sustainable coastal management and improve the planning and implementation of Nature-based Solutions worldwide.

The paper was also presented through a poster at the Ocean Sciences Meeting (OSM), where REST-COAST was represented by Carolina Gramcianinov and Joanna Staneva, colleagues from Helmholtz-Zentrum Hereon.

The full publication is available here.