Prediction of discharge coefficient of cylindrical weir–gate using GMDH-PSO

Abstract

Cylindrical weir-gate consists of a weir and a gate has been proposed for flow measurement in water engineering projects. Discharge coefficient of weir-gate is a fundamental parameter to evaluate its hydraulic efficiency. In this research, the group method of data handling (GMDH) trained by particle swarm optimization technique was used to predict the discharge coefficient of cylindrical weir-gate. The performances of prepared GMDH model were compared with multi-layer perceptron neural network (MLPNN) and support vector machine (SVM) that developed to this end, as well. Results indicated that all developed models have suitable performance, however; the SVM model was a bit more accurate. Observing the obtained structure of GMDH model showed that the upstream Froude number and ratio of opening height of gate to the diameter of cylindrical weir-gate are the most effective parameters on discharge coefficient. During the development of SVM and MLPNN models, it was found that the radial basis function as kernel function and hyperbolic tangent sigmoid as transfer function have a better accuracy compared to other tested functions.