Prediction of Discharge Coefficient for Trapezoidal Labyrinth Side Weir Using a Neuro-Fuzzy Approach

Abstract

Adaptive neuro-fuzzy inference system (ANFIS) is considered for flow over trapezoidal labyrinth side weirs located on a straight channel as a substantial part of distribution channels in irrigation systems and treatment units. To estimate the outflow over a trapezoidal labyrinth side weir, the discharge coefficient in the side weir equation needs to be determined in according with the effective dimensionless parameters which is Froude number, the sidewall angle, the ratios of weir length to channel width, weir length to total crest length and weir height to flow depth. 670 laboratory test results are used for determining discharge coefficient of trapezoidal labyrinth side weirs. The performance of the ANFIS model is compared with artificial neural networks (ANN), non-linear regression (NLR) and multi-linear regression (MLR) models. The comparing criteria used for the evaluation of the models’ performances are root mean square errors (RMSE), mean absolute errors (MAE) and determination coefficient (R2) statistics. Comparison results indicated that the ANFIS technique could be successfully employed in modeling discharge coefficient. It is found that the ANFIS model with RMSE of 0.090 in test period is superior in estimation of discharge coefficient than the nonlinear and linear regression models with RMSE of 0.124 and 0.279, respectively.