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

Hybrid Artificial Intelligence Methods for Predicting Air Demand in Dam Bottom Outlet