Ann Model for Prediction of Length of Hydraulic Jump on Rough Beds

Hydraulic jumps have immense practical utility in hydraulic engineering and allied fields, such as energy dissipater to dissipate the excess energy of flowing water downstream of hydraulic structures (spillways and sluice gates), efficient operation of flow measurement flumes, chlorinating of wastewater, aeration of streams which are polluted by biodegradable wastes and many other cases. The length of hydraulic jumps is one of the most important parameters in designing the stilling basin, however, it cannot be calculated by mathematical analyses only, experimental and laboratorial results should also be used. In this study, artificial neural network (ANN) technique was used to determine the length of the hydraulic jumps in channel having smooth and rough beds. The selected model can predict the length of jumps with high accuracy and satisfy the evaluation criteria, with root mean square error =3.2438, mean absolute percentage error =6.9231 and coefficient of determination = 0.9596. A comparison between the ANN model and empirical equation of Hughes and Flack (1984) was also done and the results showed that the ANN method is more precise.