Using data mining methods to improve discharge coefficient prediction in Piano Key and Labyrinth weirs

Abstract

As a remarkable parameter, the discharge coefficient (Cd) plays an important role in determining weirs' passing capacity. In this research work, the support vector machine (SVM) and the gene expression programming (GEP) algorithms were assessed to predict Cd of piano key weir (PKW), rectangular labyrinth weir (RLW), and trapezoidal labyrinth weir (TLW) with gathered experimental data set. Using dimensional analysis, various combinations of hydraulic and geometric non-dimensional parameters were extracted to perform simulation. The superior model for the SVM and the GEP predictor for PKW, RLW, and TLW included , and respectively. The results showed that both algorithms are potential in predicting discharge coefficient, but the coefficient of determination (RMSE, R2, Cd(DDR)max) illustrated the superiority of the GEP performance over the SVM. The results of the sensitivity analysis determined the highest effective parameters for PKW, RLW, and TLW in predicting discharge coefficients are , , and Fr respectively. HIGHLIGHTS Three different types of weirs have been studied in this paper.; Two SVM and GEP algorithms have been implemented to predict the discharge coefficient of three weirs.; Eighteen combinations of dimensionless parameters have been tested to achieve optimum prediction of the discharge coefficient.; An equation for a superior model has been extracted to simulate discharge coefficient.;

Keywords

• data-driven solver
• error distribution
• nonlinear weir
• sensitivity analysis