TY - JOUR ID - 4939 TI - Prediction of discharge coefficients for broad-crested weirs using expert systems JO - Amirkabir Journal of Civil Engineering JA - CEEJ LA - en SN - 2588-297X AU - Salmasi, Farzin AU - Nahrain, Farnaz AU - Taheri aghdam, Ali AD - Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran Y1 - 2023 PY - 2023 VL - 54 IS - 12 SP - 4435 EP - 4458 KW - Discharge Coefficient KW - Broad-crested weir KW - artificial neural network KW - Nonlinear Regression KW - M5 model tree DO - 10.22060/ceej.2022.18990.7021 N2 - 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). UR - https://ceej.aut.ac.ir/article_4939.html L1 - https://ceej.aut.ac.ir/article_4939_8477c43c9346829127e39d0c2572eeaf.pdf ER -