A multi-model ensemble approach to coastal storm erosion prediction

The accurate prediction of storm-driven coastal erosion along sandy coastlines is fundamental to addressing coastal hazards now and into the future. Here, four storm erosion models (an empirical model, the numerical models SBEACH and XBeach, and a machine learning model) were individually trained and tested on a 39-year storm erosion dataset to examine skill and error distributions. Four weighted average model ensemble approaches were also tested. The machine learning method showed the overall best skill for an individual model, followed by SBEACH, the empirical model, and XBeach. A weighted ensemble combined the models in such a way as to improve prediction (over any single model) for the largest events while maintaining comparable skill to the machine learning model during smaller events as well. These results indicate that a weighted multi-model ensemble approach can provide overall improved accuracy and reliability over a wide range of storm conditions compared to individual models.

Automated summary

This study used four different models to predict storm-driven coastal erosion and tested their skill and error distributions. The machine learning model showed the best overall skill, while the weighted ensemble approach performed well in predicting large events.