Development of a machine learning model for river bedload

Abstract. Prediction of bedload sediment transport rates in rivers is a notoriously challenging problem due to inherent variability in river hydraulics and channel morphology. Machine learning offers a compelling approach to leverage the growing wealth of bedload transport observations towards the development of a data driven predictive model. We present an artificial neural network (ANN) model for predicting bedload transport rates informed by 8,117 measurements from 134 rivers. Inputs to the model were river discharge, flow width, bed slope, and four bed surface sediment sizes. A sensitivity analysis showed that all inputs to the ANN model contributed to a reasonable estimate of bedload flux. At individual sites, the ANN model was able to reproduce observed sediment rating curves with a variety of shapes and outperformed four standard bedload models. This ANN model has the potential to be broadly applied to predict bedload fluxes based on discharge and reach properties alone.