PREDICTING SHORELINE CHANGE WITH DEEP LEARNING: CASE STUDIES FROM THE EAST AND WEST COASTS OF THE U.S.

As the coastal population continues to expand, the risk of experiencing social and economic losses due to the effects of a changing climate also increases. Although considerable advances have been achieved in terms of developing numerical process-based models for shoreline change, ensuring reliable predictions remains a formidable task. We propose an innovative approach by implementing Convolutional Neural Networks (CNNs) to predict the evolution of shorelines in response to wave forcings. While the use of these data-driven model frameworks in coastal research is still in its early stages, it holds promise in capturing the strong autocorrelation and memory/storage effects involved in shoreline evolution and may therefore offer a viable alternative to process-based models, at least in some locations. Our models were put to the test at two distinct beach locations: Ocean Beach in California, characterized by its seasonal patterns, and Duck in North Carolina, which lacks a strong seasonal signal. We offer an assessment of the models' performance through the use of absolute- value error metrics. The findings presented shed light on the potential of Deep Learning in forecasting shoreline change.