Abstract
The discharge coefficient of a modified triangular side weir is analyzed regarding various non-dimensional input sets. It is desirable to select and analyze factors or parameters that are truly relevant or the most influential to triangular side weir discharge coefficient estimation and prediction. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is applied for the selection of the most prominent triangular side weir discharge coefficient parameters based on ten input parameters. The input variables were searched using the ANFIS network to specify the input parameters’ effects on the discharge coefficients. According to the obtained results, the side weir included angle has the most effect on modeling the discharge coefficient. Then by using the selected input variables, the discharge coefficient was modeled with ANFIS, artificial neural network, support vector machine and multi non linear regression methods. The results show that ANFIS could predict the discharge coefficient significantly better than the other investigated models.
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