Document Type : Research Paper
Authors
1 Department of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.
2 Department of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran
Abstract
Generally, Hydraulic jumps usually happen at the downstream of hydraulic structures like ogee spillways. In addition, one of the parameters affecting the proper design of stilling basin is calculation of the hydraulic jump length. In this study, a hybrid method (ANFIS-DE) was proposed for modeling hydraulic jumps on sloping rough beds for first time. This approach forecasts values of the jump length by combining the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Differential Evolution (DE) algorithm. First, the variables affecting the hydraulic jump length including the ratio of bed roughness, the Froude number, the ratio of sequent depths and the bed slope were identified. Then, by combining the input parameters, five different numerical models were introduced. Furthermore, the k-fold cross validation (k=4) was utilized so as to verifying the numerical models. The results of the analysis of different numerical models indicated that the model with four input parameters (superior model) simulated the length of the hydraulic jump with higher accuracy. For the best model, the mean absolute percent error (MAPE), the correlation coefficient (R) and the root mean square error (RMSE) were predicted 4.875, 0.978 and 0.807, respectively. Finally, two parameters including the ratio of sequent depths and the Froude number were identified as the most important parameters in modeling the hydraulic jump length on sloping rough beds.
Keywords
Azimi H., Bonakdari H., Ebtehaj I., Gharabaghi B., Khoshbin F., Evolutionary design of generalized group method of data handling-type neural network for estimating the hydraulic jump roller length, Acta Mechanica 229 (2018a) 1197-1214.
Azimi H., Bonakdari H., Ebtehaj I., Michelson D.G., A combined adaptive Neuro-fuzzy inference system–firefly algorithm model for predicting the roller length of a hydraulic jump on a rough channel bed, Neural Computing and Applications 29 (2018b) 249-258.
Carollo F., Ferro V., Pampalone V., Hydraulic Jumps on Rough Beds, Journal of Hydraulic Engineering 133 (2007) 989-999.
Carollo F., Ferro V., Pampalone V., New Solution of Classical Hydraulic Jump, Journal of Hydraulic Engineering 135 (2009) 527–531.
Carollo F., Ferro V., Pampalone V., Sequent depth ratio of B-jumps on smooth and rough beds, Journal of Agricultural Engineering 44 (2013) 82-86.
Chang J.R., Wei L.Y., Cheng C.H., A hybrid ANFIS model based on AR and volatility for TAIEX forecasting, Applied Soft Computing 11 (2013) 1388-1395.
Chen M.Y., A hybrid ANFIS model for business failure prediction utilizing particle swarm optimization and subtractive clustering, Information Sciences 220 (2013) 180-195.
Ead S., Rajaratnam N., Hydraulic Jumps on Corrugated Beds, Journal of Hydraulic Engineering 128 (2002) 656-663.
Hager W.H., Hydraulic jump in non-prismatic rectangular channels, Journal of Hydraulic Research 23 (1985) 21-35.
Hager W.H., Bremen R., Kawagoshi N., Classical hydraulic jump: length of roller, Journal of Hydraulic Research, 28 (1990) 591-608.
Jang J.S.R., Sun C.T., Mizutani E., Neurofuzzy and soft computing: a computational approach to learning and machine intelligence, New Jersey: Prentice-Hall (1997).
Karbasi M., Azamathulla H.M., GEP to predict characteristics of a hydraulic jump over a rough bed, KSCE Journal of Civil Engineering 20 (2016) 3006-3011.
Kumar M., Lodhi A.S., Hydraulic jump over sloping rough floors, ISH Journal of Hydraulic Engineering 22 (2016) 127-134.
Liu J., Lampinen J., On setting the control parameter of the differential evolution method, In Proceedings of the 8th international conference on soft computing (2002) 11–18.
Naderpour H., Alavi S.A., A proposed model to estimate shear contribution of FRP in strengthened RC beams in terms of Adaptive Neuro-Fuzzy Inference System, Composite Structures 170 (2017) 215-227.
Naseri M., Othman F., Determination of the length of hydraulic jumps using artificial neural networks, Advances in Engineering Software 48 (2012) 27–31.
Omid M.H., Omid M., Esmaeeli V.M., Modelling hydraulic jumps with artificial neural networks, In Proceedings of the Institution of Civil Engineers-Water Management 158 (2005) 65–70.
Pagliara S., Lotti I., Palermo M., Hydraulic jump on rough bed of stream rehabilitation structures, Journal of Hydro-environment Research 2 (2008) 29-38.
Shoorehdeli M.A., Teshnehlab M., Sedigh A.K., Khanesar M.A., Identification using ANFIS with intelligent hybrid stable learning algorithm approaches and stability analysis of training methods, Applied Soft Computing 9 (2009) 833-850.
Storn R., Price K., Differential evolution-A simple and efficient heuristic for global optimization over continuous spaces, Journal of Global Optimization 11 (1997) 341–359.
Velioglu D., Tokyay N.D., Dincer, A.I., A numerical and experimental study on the characteristics of hydraulic jumps on rough beds, In E-proceedings of the 36th IAHR World Congress 28 (2015) 1-9.
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