ALBERT

Description: Natural systems dominated by sediment transport are notoriously difficult to forecast. This is particularly true along the ocean coastline, a region that draws considerable human attention as economic investment and infrastructure are threatened by both persistent, long-term and acute, event driven processes (i.e., sea level rise and storm damage, respectively). Forecasting the coastline's evolution over intermediate time (daily) and space (tens of meters) scales is hindered by the complexity of sediment transport and hydrodynamics, and limited access to the detailed local forcing that drives fast scale processes. Modern remote sensing systems provide an efficient, economical means to collect data within these regions. A solar-powered digital camera installation is used to capture the coast's evolution, and machine learning algorithms are implemented to extract the shoreline and estimate the daily mean intertidal coastal profile. Methods in nonlinear time series forecasting and genetic programming applied to these data corroborate that coastal morphology at these scales is predominately driven by nonlinear internal dynamics, which partially mask external forcing signatures. Results indicate that these forecasting techniques achieve nontrivial predictive skill for spatiotemporal forecast of the upper coastline profile (as much as 43% of variance in data explained for one day predictions). This analysis provides evidence that societally relevant coastline forecasts can be achieved without knowing the forcing environment or the underlying dynamical equations that govern coastline evolution.