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Prediction of bed load sediment transport rates in rivers is a notoriously difficult problem due to inherent variability in river hydraulics and channel morphology. Machine learning (ML) offers a compelling approach to leverage the growing wealth of bed load transport observations towards the development of a data-driven predictive model. We present an artificial neural network (ANN) model for predicting bed load transport rates informed by 8117 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 bed load flux. At individual sites, the ANN model was able to reproduce observed sediment rating curves with a variety of shapes without site-specific calibration. This ANN model has the potential to be broadly applied to predict bed load fluxes based on discharge and reach properties alone. [ABSTRACT FROM AUTHOR]

Predicting morphological alterations in backwater zones has substantial merit as it potentially influences the life of millions of people by the change in flood dynamics and land topography. While there is no two-dimensional river model available for predicting morphological alterations in backwater zones, there is an absolute need for such models. This study presents an integrated iterative two-dimensional fluvial morphological model to quantify spatio-temporal fluvial morphological alterations in normal flow to backwater conditions. The integrated model works through the following steps iteratively to derive geomorphic change: (1) iRIC model is used to generate a 2D normal water surface; (2) a 1D water surface is developed for the backwater; (3) the normal and backwater surfaces are integrated; (4) an analytical 2D model is established to estimate shear stresses and morphological alterations in the normal, transitional, and backwater zones. The integrated model generates a new digital elevation model based on the estimated erosion and deposition. The resultant topography then serves as the starting point for the next iteration of flow, ultimately modeling geomorphic changes through time. This model was tested on Darby Creek in Metro-Philadelphia, one of the most flood-prone urban areas in the US and the largest freshwater marsh in Pennsylvania. [ABSTRACT FROM AUTHOR]