Using a detailed set of hydrodynamic and suspended-sediment observations, we developed
data-driven algorithms based on Boosted Regression Trees and Artificial Neural Networks
to predict suspended-sediment reference (near-bed) concentration using water depth, wave-
orbital semi-excursion, wave period and bed-sediment grainsize as inputs. With one
exception, the response of the data-driven algorithms was physically sound; the exception
was the response to water depth. Outside of the range covered by the data, predictor …