  In large infrastructures such as dams, which have a relatively high economic
value, ensuring the proper operation of the associated hydraulic facilities in
different operating conditions is of utmost importance. To ensure the correct
and successful operation of the dam's hydraulic equipment and prevent possible
damages, including gates and downstream tunnel, to build laboratory models and
perform some tests are essential (the advancement of the smart sensors based on
artificial intelligence is essential). One of the causes of damage to dam
bottom outlets is cavitation in downstream and between the gates, which can
impact on dam facilities, and air aeration can be a solution to improve it. In
the present study, six dams in different provinces in Iran has been chosen to
evaluate the air entrainment in the downstream tunnel experimentally. Three
artificial neural networks (ANN) based machine learning (ML) algorithms are
used to model and predict the air aeration in the bottom outlet. The proposed
models are trained with genetic algorithms (GA), particle swarm optimization
(PSO), i.e., ANN-GA, ANN-PSO, and ANFIS-PSO. Two hydrodynamic variables, namely
volume rate and opening percentage of the gate, are used as inputs into all
bottom outlet models. The results showed that the most optimal model is
ANFIS-PSO to predict the dependent value compared with ANN-GA and ANN-PSO. The
importance of the volume rate and opening percentage of the dams' gate
parameters is more effective for suitable air aeration.
