Pareto genetic design of group method of data handling type neural network for prediction discharge coefficient in rectangular side orifices

• The method of Group Method of Data Handling (GMDH) is investigated for estimating the of a rectangular side orifice.
• By considering parameters effective on discharge coefficient, five different models were constructed.
• The genetic algorithm is applied to optimize the design of the GMDH through double targeted optimization.
• GMDH (a) providing the best results (MARE=0.021 and RMSE=0.017) in comparison with the rest of the models.

The powerful method of Group Method of Data Handling (GMDH) was used for estimating the discharge coefficient of a rectangular side orifice. First, the existing equations for calculating the discharge coefficient were studied making use of experimental results. On the first hand, the factors affecting the discharge coefficient were determined, then five models were constructed in order to analyze the sensitivity in achieving accuracy by using different parameters. The results, obtained using statistical indexes (MARE=0.021 and RMSE=0.017), showed that one model out of the five models, on estimation using the dimensionless parameters of the ratio of depth of flow in main channel to width of rectangular orifice (Ym/L), Froude number (Fr), the ratio of sill height to width of rectangular orifice (W/L) and width of main channel to width of rectangular orifice (B/L), presented the best results.