A data model to forecast the morphological evolution of multiple beach profiles

Abstract

Beaches are a natural defense against extreme events, such as storms and hurricanes, whose intensity and frequency are expected to increase in the future due to climate change. In this context, models that forecast the morphological evolution of coastal areas can be used to anticipate the effects of future scenarios, allowing early action to mitigate the damage caused by extreme events. Hence, this study included data from three different monitoring programs in data models to simulate the seasonal morphological evolution of several Portuguese beaches. Two different data models were implemented using the Random Forest algorithm. One was fed with profile data and wave conditions while the other considered also sediment size data. Both models achieved suitable performances, but the inclusion of sediment data reduced the model errors and variance, and thus improved model performance. It was demonstrated that combining data from multidisciplinary campaigns can be a solution to generate reliable and robust morphological forecasting models.