  The prediction of statistical properties of turbulent flow in large-scale
rivers is important for river flow analysis. Large-eddy simulations (LESs)
provide a powerful tool for such predictions, however, they require a very long
sampling time and significant computing power to calculate the turbulence
statistics of riverine flows. In this study, we developed encoder-decoder
convolutional neural networks (CNNs) to predict the first- and second-order
turbulent statistics of the turbulent flow of large-scale meandering rivers. We
trained the CNNs using a dataset obtained from the LES of the flood flow in a
large-scale river with three bridge piers, which formed the training testbed.
Subsequently, we employed the trained CNNs to predict the turbulent statistics
of the flood flow in a river with different bridge pier arrangements, which
formed the validation testbed. The CNN predictions for the validation testbed
river flow were compared with the simulation results of a separately performed
LES to evaluate the performance of the developed CNNs. The results showed that
the trained CNNs can successfully produce turbulent statistics of the flood
flow in large-scale rivers, such as that chosen for the validation testbed.
