Poster
In session
Postersession No. 2
,
Sept. 3, 2025,
18:15 –
19:00
Exact timing:
18:15 –
19:00
- Helmholtz-Zentrum Hereon
When applying sustainable Nature-based Solution (NbS) for coastal engineering, a major challenge lies in determining the effectiveness of these NbS approaches in mitigating coastal erosion. The efficacy of NbS is influenced by various factors, including the specific location, layout, and the scale of implementation. This study integrates artificial intelligence (AI) with hydro-morphodynamic numerical simulations to develop an AI-based emulator focused on predicting Bed Level Changes (BLC) as indicators of erosion and deposition dynamics. In particular, we explore the influence of seagrass meadows, which vary in their initial depth (hs) and depth range (hr), on the attenuation of coastal erosion during storm events.
The framework employs a hybrid approach combining the SCHISM-WWM hydrodynamic model with XBeach to simulate 180 depth range and starting depth combination (h r -h s ) scenarios along the Norderney coast in the German Bight. A Convolutional Neural Network (CNN) architecture is used with two inputs—roller energy and Eulerian velocity—to efficiently predict BLC. The CNN shows high accuracy in replicating spatial erosion patterns and quantifying erosion/deposition volumes, achieving an R² of 0.94 and RMSE of 3.47 cm during validation.
This innovative integration of AI and NbS reduces computational costs associated with traditional numerical modelling and improves the feasibility of What-if Scenarios applications for coastal erosion management. The findings highlight the potential of AI-based approach to optimize seagrass transplantation layouts and inform sustainable coastal protection strategies effectively. Future advancements aim to further optimize model integration and scalability, thereby advancing NbS applications in enhancing coastal resilience against environmental stressors.