  Flood-induced deformation of the bed topography of fluvial meandering rivers
could lead to river bank displacement, structural failure of the
infrastructures, and the propagation of scour or deposition features. The
assessment of sediment transport in large-scale meanders is, therefore, a key
environmental issue. High-fidelity numerical models provide powerful tools for
such assessments. However, high-fidelity simulations of large-scale rivers
using the coupled flow and morphodynamics modules can be computationally
expensive, owing to the costly two-way coupling between turbulence and bed
morphodynamics. This study seeks to present a novel machine learning approach,
which is trained using coupled large-eddy simulation (LES) and morphodynamics
results. The proposed machine learning approach predicts bed shear stress and
equilibrium bed morphology of large-scale meanders under bankfull flow
conditions at a fraction of the cost of coupled LES-morphodynamics. We
developed and evaluated the performance of a convolutional neural network
autoencoder (CNNAE) algorithm to generate high-fidelity bed shear stress and
equilibrium morphology of large-scale meandering rivers. The CNNAE algorithm
utilizes instantaneous shear stress distribution and change of bed elevation of
high-fidelity simulation results, along with geometric parameters of meanders
as inputs to predict mean bed shear stress distribution and equilibrium bed
elevation of rivers. The results demonstrated the feasibility, accuracy, and
efficiency of the proposed CNNAE algorithm.
