Comparative evaluation of CFD model and intelligence hybrid method to ameliorate ANFIS in side weir coefficient of discharge modelling

In this paper, the ANFIS network was optimized using three algorithms comprising the Particle Swarm Optimization (PSO), Firefly Algorithm (FFA), and Genetic algorithm (GA) for the first time. To ameliorate the ability of the numerical models,the Monte Carlo simulations were utilized. Moreover, in order to assess the simulation outcomes, the k-fold cross validation technique was implemented. Initially, using all inputs, five different parameters were used for producing theANFIS, ANFIS-GA, ANFIS-PSO, and ANFIS-FFA methods. After that, a computational fluid dynamics (CFD) model simulated the discharge coefficient (DC) and the outcome of all simulations were compared. The analysis of the results demonstrated that the ANFIS-FFA model approximates the DC with higher precision. For instance, the amount of the coefficient of determination and the scatter index were surmised as 0.961 and 0.039. Also, the side weir height ratio tothe upstream depth (P/y1) was detected as the most influential parameter. About 85% of the DC simulated by the ANFIS-FFA model had an inaccuracy of less than 5%. The performed uncertainty analysis proved that the best model possesses an underestimated efficiency. For this model, the influence of the inputs were analyzed in a ±10% range. Finally, a computational code was presented for the simulation of DC by hydraulic and environmental engineers.

Comparative evaluation of CFD model and intelligence hybrid method to ameliorate ANFIS in side weir coefficient of discharge modelling Keywords:

Computational Fluid Dynamics , Discharge Coefficient , Firefly Algorithm , Neuro-fuzzy systems , Sensitivity analysis , Side Weir