Modeling Length of Hydraulic Jump on Sloping Rough Bed using Gene Expression Programming

Determination of the hydraulic jump characteristics can play a crucial role to design an optimized stilling basin or other facilities at downstream of the hydraulic jump. in other words, the length of hydraulic jump is one the most important parameters involved to obtain the length of the structures. in contrast, the artificial intelligence techniques have been extensively applied to simulate various hydraulic problems such as hydraulic jump features. in this work, the length of hydraulic jump on sloping rough beds was predicted using the gene expression programming (gep) model for the first time because the gep model could provide an explicit equation to estimate the target function. the monte carlo simulations were used to enhance the capability of the gep model. in addition, k-fold cross-validation was employed in order to verify the results of the gep model. in order to determine the length of hydraulic jump, five different gep models were introduced using the input parameters. then by analyzing the gep model results, the superior model was presented. for the superior model, the correlation coefficient, mean absolute percentage error, and root mean square error were computed to be 0.901, 11.517, and 1.664, respectively. according to the sensitivity analysis, the froude number at upstream of hydraulic jump was identified as the most important parameter to model the length of hydraulic jump. furthermore, the partial derivative sensitivity analysis (pdsa) was performed. for instance, pdsa was calculated as positive for all the input variables.