Azam Akhbari - Department of Civil Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia.
Amir Hossein Zaji - Young Researches Club, Kermanshah Islamic Azad University.
Hamed Azimi - Young Researches Club, Kermanshah Islamic Azad University.
Mohsen Vafaeifard - Department of Civil Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia

Weirs are installed on open channels to adjust and measure the flow. Also, discharge coefficient is considered as the most important hydraulic parameter of a weir. In this study, using the Radial Base Neural Networks (RBNN) and M5 methods, the discharge coefficient of triangular plan form weirs is modeled. At first, the effective parameters in the prediction of the discharge coefficient are identified. Then, by combining the input parameters, for each of the RBNN and M5 methods, six different models are introduced. By analyzing the modeling results for all models, it was shown that the M5 model is capable of modeling the discharge coefficient more accurately. Also, based on the modeling results, a model that considered the impact of all input parameters was introduced as a superior model. The mean absolute percentage error (MAPE) and correlation coefficients (R2) values for the preferred model in the test mode were calculated 2.774 and 0.831, respectively. Also, for each of the M5 models, some relationships were proposed to estimate the triangular plan form weirs. The evaluation of these relationships showed that the parameters of the ratio of head over the weir to channel width (h/B) and Froude number (Fr) were the most effective parameters in the prediction of the discharge coefficient.

Triangular plan form weir, Discharge coefficient, Radial basis neural networks, M5’ method, Sensitivity analysis