Neural networks for estimation of discharge capacity of triangular labyrinth side-weir located on a straight channel |
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| Authors: | M Emin Emiroglu Omer Bilhan Ozgur Kisi |
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| Affiliation: | 1. Firat University, Civil Engineering Department, 23119 Elazig, Turkey;2. Erciyes University, Civil Engineering Department, 38019 Kayseri, Turkey;1. Faculty of Civil and Environmental Engineering, Tarbiat Modares University, 14115-115 Tehran, Iran;2. Faculty of Engineering, Kharazmi University, 15719-14911 Tehran, Iran;3. Water Engineering Research Institute, Faculty of Civil and Environmental Engineering, Tarbiat Modares University, 14115-397 Tehran, Iran;1. Department of Civil Engineering, Bitlis Eren University, 13100 Bitlis, Turkey;2. Department of Civil Engineering, Firat University, 23119 Elazig, Turkey;1. Civil Engineering Department, Bitlis Eren University, Bitlis, Turkey;2. Civil Engineering Department, Firat University, Elazig, Turkey;1. Department of Civil Engineering, Nevsehir HBV University, Nevsehir, Turkey;2. Department of Civil Engineering, Firat University, Elazig, Turkey;3. Department of Civil and Environmental Engineering, Wayne State University, Detroit, MI, USA;4. Department of Software Engineering, Firat University, Elazig, Turkey |
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| Abstract: | Side-weirs are flow diversion devices widely used in irrigation, land drainage, and urban sewage systems. It is essential to correctly predict the discharge coefficient for hydraulic engineers involved in the technical and economical design of side-weirs. In this study, the discharge capacity of triangular labyrinth side-weirs is estimated by using artificial neural networks (ANN). Two thousand five hundred laboratory test results are used for determining discharge coefficient of triangular labyrinth side-weirs. The performance of the ANN model is compared with multi nonlinear regression models. Root mean square errors (RMSE), mean absolute errors (MAE) and correlation coefficient (R) statistics are used as comparing criteria for the evaluation of the models’ performances. Based on the comparisons, it was found that the neural computing technique could be employed successfully in modelling discharge coefficient from the available experimental data. There were good agreements between the measured values and the values obtained using the ANN model. It was found that the ANN model with RMSE of 0.0674 in validation stage is superior in estimation of discharge coefficient than the multiple nonlinear and linear regression models with RMSE of 0.1019 and 0.1507, respectively. |
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