Since many years ago, flow measurement has become a fundamental issue in hydraulic engineering. One of the conventional methods of flow measurement is the use of combined structures. In this regard, using a combined structure, including a gate and a weir, is one of the approaches that has attracted the attention of researchers in this field. Therefore, in this research, five different methods based on artificial neural networks were used to predict the discharge coefficient. The networks architecture includes an input layer with four neurons, a hidden layer with seven neurons, and an output layer with one neuron. be mentioned that the number of neurons within the hidden layer is set to 4 only for the recurrent network. For the hidden layer, the logarithmic sigmoid activation function was used. Also, the linear activation function was used for the output layer. Finally, the results showed that the Levenberg-Marquardt (LM) algorithm performs better than the other methods. The convergence speed of this algorithm, which also uses the second derivative, is much higher than others. In this case, the coefficient of determination (R^2) for the training and the test stage was equal to 0.92616 and 0.94079, respectively. In addition to, the first type of rough model with the gradient descent training algorithm also had an acceptable performance and was placed in second place. Also, the sensitivity analysis on the dimensionless parameters affecting this issue showed that the H⁄d, y⁄d, b⁄B, and b⁄d parameters have maximum to minimum effect on the model results, respectively.
Nouri, M., & Hemmati, M. (2020). Discharge coefficient in the combined weir-gate structure. Flow Measurement and Instrumentation, 75, 101780.
Chen, W., Sharifrazi, D., Liang, G., Band, S. S., Chau, K. W., & Mosavi, A. (2022). Accurate discharge coefficient prediction of streamlined weirs by coupling linear regression and deep convolutional gated recurrent unit. Engineering Applications of Computational Fluid Mechanics, 16(1), 965-976.
Safari, S., Takarli, A., Salarian, M., Banejad, H., Heydari, M., & Ghadim, H. B. (2022). Evaluation of ANN, GEP, and Regression Models to Estimate the Discharge Coefficient for the Rectangular Broad-Crested Weir. Polish Journal of Environmental Studies, 31(5).
Sahib, J. H., Al-Waeli, L. K., & Al Rammahi, A. H. J. (2022). Utilization of ANN technique to estimate the discharge coefficient for trapezoidal weir-gate. Open Engineering, 12(1), 142-150.
Parsaie, A., Haghiabi, A. H., Emamgholizadeh, S., & Azamathulla, H. M. (2019). Prediction of discharge coefficient of combined weir-gate using ANN, ANFIS and SVM. International Journal of Hydrology Science and Technology, 9(4), 412-430.
Aein, N., Najarchi, M., Mirhosseini Hezaveh, S. M., Najafizadeh, M. M., & Zeighami, E. (2020, October). Simulation and prediction of discharge coefficient of combined weir–gate structure. In Proceedings of the Institution of Civil Engineers-Water Management (Vol. 173, No. 5, pp. 238-248). Thomas Telford Ltd.
Ahmed, F. H. (1985). Characteristics of discharge of the combined flow through sluice gates and over weirs. J. Engineering and Technology, Iraq, 3(2), 49-63.
Negm, A. M., El-Saiad, A. A., Alhamid, A. A., & Husain, D. (1995). Characteristics of simultaneous flow over weir and below inverted V-Notches. Civil Engineering Research Magazine, Civil Engineering Department, Faculty of Engineering, Al-Azhar University, Cairo, Egypt, 16(19), 786-799.
Hayawi, H. A. A. M., Yahia, A. A. A. G., & Hayawi, G. A. M. (2008). Free combined flow over a triangular weir and under rectangular gate. Damascus university journal, 24(1), 9-22.
Wang, F., Zheng, S., Ren, Y., Liu, W., & Wu, C. (2022). Application of hybrid neural network in discharge coefficient prediction of triangular labyrinth weir. Flow Measurement and Instrumentation, 83, 102108.
Bilhan, O., Emiroglu, M. E., & Kisi, O. (2011). Use of artificial neural networks for prediction of discharge coefficient of triangular labyrinth side weir in curved channels. Advances in Engineering Software, 42(4), 208-214.
Salmasi, F., Nahrain, F., Abraham, J., & Taheri Aghdam, A. (2023). Prediction of discharge coefficients for broad-crested weirs using expert systems. ISH Journal of Hydraulic Engineering, 29(1), 1-11.
Parsaie, A., Haghiabi, A. H., Saneie, M., & Torabi, H. (2017). Predication of discharge coefficient of cylindrical weir-gate using adaptive neuro fuzzy inference systems (ANFIS). Frontiers of Structural and Civil Engineering, 11(1), 111-122.
Lucas C., Abbaspour A., Gholipour A., Araabi B., Fatourechi M. (2003). “Enhancing the performance of neurofuzzy predictors by emotional learning algorithm”, Informatica, 27, 137–145.
Roweis, S. (1996). Levenberg-marquardt optimization. Notes, University Of Toronto, 52.
Lingras, P. (1996, July). Rough neural networks. In Proc. of the 6th Int. Conf. on Information Processing and Management of Uncertainty in Knowledgebased Systems (pp. 1445-1450).
Afzali Ahmadabadi, S., & Vatani, A. (2023). A study of five types of ANN-based approaches to predict discharge coefficient of combined weir-gate. Journal of Hydraulic Structures, 8(4), 73-92. doi: 10.22055/jhs.2023.43234.1246
Sanaz Afzali Ahmadabadi; Arash Vatani. "A study of five types of ANN-based approaches to predict discharge coefficient of combined weir-gate". Journal of Hydraulic Structures, 8, 4, 2023, 73-92. doi: 10.22055/jhs.2023.43234.1246
Afzali Ahmadabadi, S., Vatani, A. (2023). 'A study of five types of ANN-based approaches to predict discharge coefficient of combined weir-gate', Journal of Hydraulic Structures, 8(4), pp. 73-92. doi: 10.22055/jhs.2023.43234.1246
Afzali Ahmadabadi, S., Vatani, A. A study of five types of ANN-based approaches to predict discharge coefficient of combined weir-gate. Journal of Hydraulic Structures, 2023; 8(4): 73-92. doi: 10.22055/jhs.2023.43234.1246