  Machine learning methods have been extensively used to study the dynamics of
complex fluid flows. One such algorithm, known as adaptive neural fuzzy
inference system (ANFIS), can generate data-driven predictions for flow fields
but has not been applied to natural geophysical flows in large-scale rivers.
Herein, we demonstrate the potential of ANFIS to produce three-dimensional (3D)
realizations of the instantaneous flood flow field in several large-scale,
virtual meandering rivers. The 3D dynamics of flood flow in large-scale rivers
were obtained using large-eddy simulation (LES). The LES results, i.e., the 3D
velocity components, were employed to train the learnable coefficients of an
ANFIS. The trained ANFIS, along with a few time-steps of LES results (precursor
data) were then used to produce 3D realizations of flood flow fields in
large-scale rivers with geometries other than the one the ANFIS was trained
with. We also used the trained ANFIS to generate 3D realizations of river flow
at a discharge other than that the ANFIS was trained with. The flow field
results obtained from ANFIS were validated using separate LES runs to assess
the accuracy of the 3D instantaneous realizations of the machine learning
algorithm. An error analysis was conducted to quantify the discrepancies among
the ANFIS and LES results for various flood flow predictions in large-scale
rivers.
