Modeling of pressure fluctuation beneath hydraulic jump

Various types of hydraulic jump occurring on horizontal and sloping channels have been analyzed experimentally and theoretically, and the results are available in the literature. In this study, Artificial Neural Network (ANN) models were developed to simulate the mean pressure fluctuations beneath hydraulic jump occurring on horizontal stilling basins. Multilayers feed forward neural network with back propagation learning algorithm is used to model the pressure fluctuations beneath hydraulic jump. Explicit formulations of mean pressure fluctuation and dimensionless pressure fluctuation parameter in terms of the most contributing characteristics of hydraulic jump occurring on stilling basins are presented. The proposed neural network models are compared with nonlinear regression models that were developed using considered physical parameters. The results of the neural network modelling are found to be superior over the regression models and are in good agreement with the experimental results due to relatively small values of error (mean absolute percentage error). Keywords: Neural networks, pressure fluctuations, hydraulic jump, stilling basin, explicit neural networks formulation, regression analysis.