A MIXTURE OF EXPERTS APPROACH COMBINING PHYSICS INFORMED AND MACHINE LEARNING SHORELINE MODELS

Understanding the drivers, as well as the ability to predict sandy shoreline change, is of primary interest to coastal engineers and managers, alike. As machine learning has increasingly gained prominence in the field of environmental science, there has also been a growing emphasis on the need for models that are more interpretable. Striking the right balance between model performance and interpretability has become increasingly nuanced. After all, establishing trust in a model necessitates a degree of comprehension regarding its underlying decision-making process. One path forward lies in the co-evolution of both methods to arrive at a modelling approach that retains interpretability and also captures the relevant dynamics to make accurate forecasts. In this study we develop several models combining multiple approaches designed to promote interpretability while retaining high predictive performance. Here we hypothesize that a smart mixture gate informed by prior knowledge will allow more flexibility in capturing the different modes of shoreline change which make up the overall response.