title: Investigating appropriate artificial intelligence approaches to reliably predict coastal wave overtopping and identify process contributions
abstract: • Random forests predicted wave overtopping occurrence with over 95 % accuracy. • Significant wave height, freeboard, and wind speed mainly influence predictions. • Random forests outperformed EurOtop and process-based modelling. • Crucial wind effects overlooked by existing methods likely enhanced predictions. Predicting coastal wave overtopping is a significant challenge, exacerbated by climate change, increasing the frequency of severe flooding and rising sea levels. Digital twin technologies
journal: Ocean Modelling