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

Prediction of three-dimensional flood-flow past bridge piers in a large-scale meandering river using convolutional neural networks