Modeling Hydraulic Jump Length on Sloping Rough Beds using Adaptive Neuro Fuzzy Inference Systems – Genetic Algorithm

In general, rapid transformation of supercritical flow regime into subcritical flow is accompanied with hydraulic jump. The phenomenon usually occurs at downstream of the hydraulic structures such as ogee spillway. The length of hydraulic jump is one of the most important parameters used to determine the dimension of stilling basins. In current study, a hybrid method for predicting the hydraulic jump length on sloping rough bed was developed. In the other words, the hybrid method (ANFIS-GA) was presented using combination of Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Genetic Algorithm (GA). In this study, to examine the performance of ANFIS-GA models, the Monte Carlo simulation (MCs) was used. At first, the effective parameters on length of hydraulic jump such as; Froude number in upstream of the hydraulic jump, the ratio of bed roughness, sequent depth ratio, and bed slope, were identified. Next, regarding these parameters, five ANFIS-GA models were defined. Then, the results of the ANFIS-GA models were examined and the superior model was introduced. The superior model predicted the experimental measurements with acceptable accuracy. For example, the Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) were respectively computed 4.520 and 0.781. In addition, the results of modeling revealed that the Froude number at upstream of hydraulic jump was the most effective parameter in modeling the length of hydraulic jump on sloping rough bed using ANFIS-GA method.