Optimization of Fuzzy Systems using Genetic Algorithm for modeling the Hydraulic Jump Length on Sloping rough beds

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Abstract

< p >In general, rapid transformation of supercritical flow regime into subcritical flow is accompanied with hydraulic jump. The phenomenon usually occurs at downstream of hydraulic structures such as ogee spillway. Also, the length of hydraulic jump is one of the most important parameters in determining the dimension of stilling basins. In current study, a hybrid method for prediction the length of hydraulic jump 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 at upstream of hydraulic jump, the ratio of bed roughness, sequent depth ratio, and bed slope were identified. Next, regarding the parameters, five ANFIS-GA models were defined. Then, the results of the ANFIS-GA models were examined that the superior model was introduced. The superior model predicts the experimental measurement 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 is the most effective parameters in modeling the length of hydraulic jump on sloping rough bed using ANFIS-GA method.

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References

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Water and Soil Science
Volume 29, Issue 4
February 2020
Pages 189-201

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