alphaBeach: Self-attention-based spatiotemporal network for skillful prediction of shoreline changes multiple days ahead

Abstract

We developed a self-attention-based spatiotemporal network called $alpha$Beach that uses spatiotemporal representation learning for skillful prediction of shoreline changes multiple days ahead. The proposed model predicts the spatiotemporal position of the shoreline up to seven consecutive days in the future based on hydrodynamic forcing of ocean waves and tide data for the past 30 consecutive days. It is further divided into $alpha$Beach-w/o IC and $alpha$Beach-w/ IC depending on whether or not the beach state of the antecedent historical shoreline information is used as the initial condition. $alpha$Beach-w/o IC, which does not incorporate this information, learns the sequential relationship between hydrodynamic forcing and shoreline to estimate overall trends of shoreline changes including seasonal oscillation from the point in time after model training, given only the ocean waves and tides. $alpha$Beach-w/ IC does incorporate antecedent historical shoreline information to greatly enhance its predictive accuracy of shoreline progradation, retreat, and beach rotation for short-term time scales and for extreme storm events. The proposed model was applied to Tairua Beach, New Zealand, and it demonstrated superior predictive accuracy compared to previous methods and matched current understanding of accretion-dominated and oscillation-dominated shoreline changes.