Volume 27, Issue 3 (Fall 2023)
jwss 2023, 27(3): 179-199 |
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Majedi Asl M, Omidpour Alavian T, Kouhdaragh M, Shamsi V. Comparison of Hydraulic Efficiency of Arched Non-linear Weirs in Plan Using GEP and SVM Neural Networks. jwss 2023; 27 (3) :179-199
URL: http://jstnar.iut.ac.ir/article-1-4328-en.html
URL: http://jstnar.iut.ac.ir/article-1-4328-en.html
Maragheh university , mehdi.majedi@gmail.com
Abstract: (1311 Views)
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.
Keywords: Neural networks, Non-linear weirs, Discharge coefficient, Support vector machine, Genetic expression tool
Type of Study: Research |
Subject:
Ggeneral
Received: 2023/01/17 | Accepted: 2023/06/10 | Published: 2023/12/1
Received: 2023/01/17 | Accepted: 2023/06/10 | Published: 2023/12/1
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