عنوان مقاله [English]
The purpose of this research is to investigate the amount of relative energy loss (EDR) in zigzag weirs with triangular and trapezoidal plans in different dimensions using Support Vector Machine (SVM) model, Random Forest (RF) algorithm, and Artificial Neural Network (ANN) method. 70% of the experimental data sets were used for the training phase and 30% for the test phase. In the SVM model, the results of different kernels showed that the Radial Basis Function (RBF) kernel has better results in predicting the relative energy loss of zigzag weirs compared to the Polynomial, Linear, and Sigmoid kernels. The results of statistical indicators of correlation coefficient (R), percentage Mean Relative Error (Mean RE%), Root Mean Square Error (RMSE), and Kling Gupta Efficiency (KGE) for the SVM-RBF model in the test phase are 0.907, 1.38%, 0.0153, and 0.744, respectively. In the ANN method, the Multi-Layer Perceptron (MLP) network has more accurate results compared to the RBF network. The results of the above indicators in the test phase for the ANN-MLP method are 0.969, 0.73%, 0.007, and 0.968, respectively. In addition, these results for the RF model are 0.878, 1.78%, 0.0192, and 0.362, respectively. Examining the results showed that the ANN method performs better than other SVM and RF models.