Application of Artificial Neural Network and M5 model tree for determination of discharge coefficients in broad crested weirs

Broad-crested weirs can be used to make discharge measurements in irrigation canals; the entrance of stepped weirs or chutes is sometimes designed as a broad-crested weir structure. These structures are also sometimes used for the dam body. In this study, the Artificial Neural Network (ANN) and M5 model tree methods are used to predict discharge coefficients (Cd ) for broad-crested weirs. The results from these two models are compared with nonlinear regression equations. Four series of data obtained from different rectangular broad-crested weirs have been used and important dimensionless parameters have been defined. Results show that the ANN procedure is superior to the M5 model and regression approaches. The accuracy for ANN is quantified by R=0.966 and RMSE=0.038. All three methods are able to provide a reasonable prediction for Cd ; the M5 model tree provides four linear equations that can be used to estimate Cd . The shape of the Cd contours shows that the effect of weir height (P) exceeds that of the weir length (L).