Application of two different neural network techniques to lateral outflow over rectangular side weirs located on a straight channel

Side weirs are structures often used in irrigation techniques, sewer networks and flood protection. This study aims to obtain sharp-crested rectangular side weirs discharge coefficients in the straight channel by using artificial neural network model for a total of 843 experiments. The performance of the feed forward neural networks (FFNN) and radial basis neural networks (RBNN) are compared with multiple nonlinear and linear regression models. Root mean square errors (RMSE), mean absolute errors (MAE) and correlation coefficient (R) statistics are used for the evaluation of the models’ performances. Comparison results indicated that the neural computing techniques could be employed successfully in modeling discharge coefficient. The FFNN is found to be better than the RBNN. It is found that the FFNN model with RMSE of 0.037 in test period is superior in estimation of discharge coefficient than the multiple nonlinear and linear regression models with RMSE of 0.054 and 0.106, respectively.