Predicting trapezoidal and rectangular side weirs discharge coefficient using machine learning methods

Abstract

Side weirs are useful and common devices in flow measurement issues. These weirs have been extensively used in hydraulic and environmental engineering applications. The characteristic of flow over side weirs are completely different from that over a weir normal to the approach channel. An accurate flow modeling over a side weir mainly depends on the proper estimation of discharge coefficient. In this study, the potential of two different machine learning methods, namely support vector machines combined with genetic algorithm (SVM–GA) and gene expression programming (GEP) were evaluated for predicting trapezoidal and rectangular sharp-crested side weirs discharge coefficient. Correlation coefficient (R), mean normalize error (MNE), and Nash–Sutcliffe index (NS) statistics are used for the evaluation of the model’s performances. The results showed that the SVM–GA model with R = 0.97, NS = 0.94, and MNE = 13% for trapezoidal side weir and R = 0.97, NS = 0.91, and MNE = 18% for rectangular side weir, in test period gives more accurate results than GEP. The results verify that the SVM–GA model can process used data series by better generalization ability and higher prediction accuracy.