Gaussian process regression approach for predicting wave attenuation through rigid vegetation

Numerical modelling in the coastal environment often requires highly skilled users and can be hindered by high computation costs and time requirements. Machine Learning (ML) techniques have the potential to overcome these limitations and complement existing methods. This is an exploratory investigation utilising a Gaussian Process (GP) data -driven modelling approach that can reproduce, for the given range of conditions in this study, the results of a widely used process -based model, XBeachX, when applied to the challenging problem of wave attenuation through vegetation. This study utilises efficient sampling strategies for data exploration, providing a valuable framework for future studies. The GP model was trained on a synthetic dataset generated using the numerical model XBeachX, which was calibrated using laboratory measurements. Our findings indicate that welltrained ML models can strongly complement traditional modelling approaches, especially in an environment where data sources are increasingly available. We have also explored the underlying interactions of the GP model's input features and their relationship to the model's output through a sensitivity analysis.