فهرست مطالب توحید امیدپور علویان

Non-linear weirs meanwhile economic advantages, have more passing flow capacity than linear weirs. These weirs have higher discharge efficiency with less free height upstream compared to linear weirs by increasing the length of the crown at a certain width. Intelligent algorithms have found a valuable place among researchers due to their great ability to discover complex and hidden relationships between effective independent parameters and dependent parameters, as well as saving money and time. In this research, the performance of support vector machine (SVM) and gene expression programming algorithm (GEP) in predicting the discharge coefficient of arched non-linear weirs was investigated using 243 laboratory data series for the first scenario and 247 laboratory data series for the second scenario. The geometric and hydraulic parameters were used in this research including the water load (HT), weir height (P), total water load ratio (HT/p), arc cycle angle (Ɵ), cycle wall angle (α), and discharge coefficient (Cd). The results of artificial intelligence showed that the combination of parameters (Cd, H_T/p, α, Ɵ) respectively in GEP and SVM algorithms in the training phase related to the first scenario (Labyrinth weir with cycle wall angle 6 degrees) were respectively equal to (R2=0.9811), (RMSE=0.02120), (DC=0.9807), and (R2=0.9896), (RMSE=0.0189), (DC=0.9871) in the second scenario (Labyrinth weir with a cycle wall angle of 12 degrees) it was equal to (R2=0.9770), (RMSE=0.0193), (RMSE=0.9768), and (R2 = 0.9908), (RMSE = 0.0128), (DC = 0.9905), which compared to other combinations has led to the most optimal output that shows the very favorable accuracy of both algorithms in predicting the coefficient the Weir discharge is arched non-linear. The results of the sensitivity analysis indicated that the effective parameter in determining the discharge coefficient of the arched non-linear Weir in GEP and in SVM is the total water load ratio parameter (HT/p). Comparing the results of this research with other researchers revealed that the evaluation indices for GEP and SVM algorithms of this research had better estimates than other researchers.

While having economic advantages, non-linear weirs have more passing flow capacity than linear weirs. These weirs have higher discharge efficiency with less free height upstream compared to linear weirs by increasing the length of the crown at a certain width. Intelligent algorithms have found a valuable place among researchers due to their great ability to discover complex and hidden relationships between effective independent parameters and dependent parameters, as well as saving money and time. In this research, the performance of support vector machine (SVM), gene expression programming (GEP), software (QNET) and artificial intelligence network (ANN) in predicting the discharge coefficient of non-linear Weirs of 318 data series for the first scenario And the second scenario includes the number of 363 data series and the third scenario includes data integration (the sum of the first and second scenario) which includes 681 data series. The difference between the first and second scenarios is in the shape of the quarter-circle and semi-circle weir crown. The geome tric and hydraulic lines used in this research include total water load ratio (H_T/p), magnification) L_C/W), cycle wall angle (α) and discharge coefficient (Cd). The results of artificial intelligence showed that the combinations (Cd, H_T/p, α, L_C/W) in QNET, ANN, GEP, SVM algorithms in the training stage related to the superior scenario are equal to the evaluation indicators respectively (R2=0.9960), (RMSE=0.0080), (DC=0.9961), (R2=0.9980), (RMSE=0.0057), (DC=0.9980), (R2=0.9837), (RMSE=0.0207), (DC=0.9838) and (R2=0.9902), (RMSE=0.0186), (DC=0.9830). Which has led to the most optimal output compared to other combinations, which indicates a very favorable accuracy in all four methods, namely ANN, QNET, SVM and GEP in predicting the weir discharge coefficient is non-linear. The results of the sensitivity analysis showed that the effective parameter in determining the nonlinear weir discharge coefficient in all methods is the total water load ratio parameter (H_T/p).