Support vector machine (SVM) approach to develop the discharge prediction model for triangular labyrinth weir

Abstract

Abstract Most of the studies on labyrinth weir were carried out in the laboratory, and regression models have been developed for discharge coefficient in terms of pertinent independent parameters. It is difficult to obtain an exact analytical solution to the head discharge relationship due to the existence of 3D flow. Consequently, various forms of soft computing techniques are used as an appropriate alternative to achieve greater accuracy in developing a discharge prediction model. In the present study, support vector regression (SVR) has, therefore, been implemented to develop a discharge coefficient prediction model for a triangular labyrinth (TL) weir using a sizeable amount of laboratory data available in the literature. An attempt has also been made to obtain a simple discharge coefficient equation using the same data based on the non-linear regression (NLR) approach for field application. A comparative study has been carried out to assess the accuracy of the discharge coefficient models obtained in the present study and those reported in the literature. Sensitivity analysis has been made to study the influence of individual parameters on the discharge coefficient. The accuracy of different discharge coefficient prediction models was also tested for the data of prototype labyrinth weir and appropriate models were recommended for the field application.