Document Type : Article
Authors
1
Ph.D. Student, Dept. of Civil Engineering, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran
2
Department of Civil Engineering, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran.
3
Department of Civil Engineering, Faculty of Engineering, University of Maragheh, Maragheh, Iran
Abstract
The use of sills with the gates leads to a reduction in the height of the gate. The sills affecting the flow, change its quantities, especially the discharge coefficient. In the present research, the amount of discharge coefficient of the sluice gate is examined for the first presented theoretical relationship in a non-suppressed sill state; to measure its performance using soft computing methods. For the models, 70% of the data were used for the training phase and the rest for the testing phase. The results of statistical indicators showed that in all SVM, KNN, GEP, and ANN models, the model with all input parameters was recognized as the superior model. In the SVM model, the results of various kernels showed that the Radial Basis Function kernel has better results in predicting the discharge coefficient compared to the Polynomial, Linear, and Sigmoid kernels. The results of the correlation coefficient (R), Root Mean Square Error (RMSE), mean percentage Relative Error (MRE%), and Kling Gupta Efficiency (KGE) in the test stage for the SVM model were 0.96, 0.018, 0.90, and 1.92%, respectively. The results of the neighbor coefficient (K) showed that in the K equal 2, the RMSE and MRE had a lower value and were close to the experimental results. In addition, in the KNN model, among distance criteria measures (Manhattan, Euclidean, Euclidean Squared, and Chebychev), the Manhattan criteria has a higher accuracy in predicting the discharge coefficient than the other ones. For this model, the results were 0.97, 0.016, 0.96, and 1.70%, respectively in the testing phase. In addition, the results for the GEP model were 0.98, 0.019, 0.85, and 2.28%, respectively. In the present research, the ANN method is more accurate compared to SVM, GEP, and KNN models; so that, for the ANN model, the KGE was in the very good range.
Keywords
Main Subjects
)