Data-intelligence approaches for comprehensive assessment of discharge coefficient prediction in cylindrical weirs: Insights from extensive experimental data sets - Roushangar, Kiyoumars; Shahnazi, Saman; Mehrizad, Amir (Measurement : journal of the International Measurement Confederation, 2024)
•Kernel-based models such as SVM, GPR, KELM, and KRR were employed, alongside neural network-based models including FFBP, CFBP, GRNN, and RBFN for discharge coefficient prediction of cylindrical weir.•855 experimental data points from different types of cylindrical weir used to feed the utilized models.•The predictive capability of discharge coefficient for various types of cylindrical weirs was discussed.•The prediction performance of employed data-intelligence techniques was assessed under varied hydraulic conditions. The present study collected a wide range of experimental data samples, including 855 records from various types of cylindrical weirs under diverse hydraulic conditions, setting the stage for an in-depth analysis for modeling of discharge coefficient. For this purpose, the prediction capability of four kernel-based methods (SVM, GPR, KELM, and KRR) and four neural network-based methods (FFBP, CFBP, GRNN, and RBFN) was thoroughly investigated. Results reveal that among the neural network methods, FFBP demonstrates superior performance with a correlation coefficient (R) of 0.973, Nash-Sutcliffe Efficiency (NSE) of 0.942, and Mean Squared Error (RMSE) of 0.014, particularly excelling at high flow rates. Conversely, KRR emerges as the top-performing kernel-based method with R = 0.901, NSE = 0.811, and RMSE = 0.058. Moreover, the prediction process unveils challenges associated with hydraulic conditions, notably the presence of upstream ramps, which substantially diminish the accuracy of modeling results by 112 %.