Support Vector Machine to predict the discharge coefficient of Sharp crested w-planform weirs

In this paper, the discharge coefficient (Cd) of triangular labyrinth weir was predictedusing Multilayer Perceptron Neural Network (MLPNN), Radial Basis Neural Network (RBFNN) andsupport vector machine (SVM). To this end, 223 data sets related to the effective parameters on Cd werecollected. Using dimensional analysis techniques, the involved dimensionless parameters on Cd werederived. To find out the most effective parameters on Cd, the Gamma test (GT) was analyzed. Results ofGT demonstrated that H/P, Lw/Lc, and Lw/Wm are the most effective parameters on Cd. To develop ANNand SVM, different types of transfer and kernel functions were tested. During the testing of transfer andkernel functions for developing the ANN and SVM models, respectively, it was found that tensing andRBFNN have the best performance for predicting the Cd. Overall evaluation of the results of developedmodels indicated that both models have a suitable accuracy in predicting the Cd; however, the SVM is abit more accurate. Comparing the outcomes of the applied models in terms of DDR index shows that thedata dispersivity of SVM is less than the others; therefore, this model is more reliable.