Abstract
Open-channel junctions are one of the most significant and practical structures in hydraulic engineering. Due to erosion in the contraction zone and sediment deposition in the separation zone, the ability to know the flow velocity in each point’s coordinate of the junctions’ main channel is a vital topic in designing them. This study aims at modeling the flow velocity in junctions using the coordinates of each point (x*, y*, and z*) and the main channel upstream to downstream discharge ratio (q*). In the modeling procedure, four powerful and well-known simulation models, namely radial basis neural network (RBNN), gene expression programming (GEP), group method of data handling (GMDH), and nonlinear regression model are used. One of the main goals of the present study is to develop accurate, simple, and explicit equations for simulation of the junctions’ main channel velocity to use in practical situations. So that, all of the input variables are dimensionless to use in designing procedure of junctions with any scales. The results showed that using a simple and practical equation, GEP model with mean square error (MSE) value of 0.055 is more accurate in predicting longitudinal velocity in open-channel junctions than RBNN, GMDH, and the regression models with MSE values of 0.063, 0.075, and 0.103, respectively.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Ab Ghani A, Md Azamathulla H (2011) Gene-expression programming for sediment transport in sewer pipe systems. J Pipeline Syst Eng Pract 2:102–106. https://doi.org/10.1061/(ASCE)PS.1949-1204.0000076
Abdi A, Hassanzadeh Y, Talatahari S, Fakheri-Fard A, Mirabbasi R (2017a) Parameter estimation of copula functions using an optimization-based method. Theoret Appl Climatol 129:21–32
Abdi A, Hassanzadeh Y, Talatahari S, Fakheri-Fard A, Mirabbasi R (2017b) Regional bivariate modeling of droughts using L-comoments and copulas. Stoch Env Res Risk Assess 31:1199–1210
Abdi A, Hassanzadeh Y, Talatahari S, Fakheri-Fard A, Mirabbasi R, Ouarda TB (2017c) Multivariate regional frequency analysis: two new methods to increase the accuracy of measures. Adv Water Resour 107:290–300
Aytek A, Kisi O (2008) A genetic programming approach to suspended sediment modelling. J Hydrol 351:288–298. https://doi.org/10.1016/j.jhydrol.2007.12.005
Azamathulla HM, Ahmad Z, Ghani A (2013) An expert system for predicting Manning’s roughness coefficient in open channels by using gene expression programming. Neural Comput Appl 23:1343–1349. https://doi.org/10.1007/s00521-012-1078-z
Best JL, Reid I (1984) Separation zone at open-channel junctions. J Hydraul Eng 110:1588–1594. https://doi.org/10.1061/(ASCE)0733-9429(1984)110:11(1588)
Bilhan O, Emin Emiroglu M, Kisi O (2010) Application of two different neural network techniques to lateral outflow over rectangular side weirs located on a straight channel. Adv Eng Softw 41:831–837. https://doi.org/10.1016/j.advengsoft.2010.03.001
Bonakdari H, Zaji AH (2016) Open channel junction velocity prediction by using a hybrid self-neuron adjustable artificial neural network. Flow Meas Instrum 49:46–51. https://doi.org/10.1016/j.flowmeasinst.2016.04.003
Broomhead DS, Lowe D (1988) Radial basis functions, multi-variable functional interpolation and adaptive networks. Royal Signals and Radar Establishment Memorandum 4248
Buhmann MD (2003) Radial basis functions: theory and implementations, vol 5. Cambridge University Press, Cambridge
Burney SA, Ali SM, Burney S (2017) A survey of soft computing applications for decision making in supply chain management. In: 2017 IEEE 3rd international conference on engineering technologies and social sciences (ICETSS). IEEE, pp 1–6
Ferreira C (2001) Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst 13:87–129
Ferreira C (2002) Gene expression programming in problem solving. Soft computing and industry. Springer, Berlin, pp 635–653
Gholami A, Bonakdari H, Zaji AH, Michelson DG, Akhtari AA (2016) Improving the performance of multi-layer perceptron and radial basis function models with a decision tree model to predict flow variables in a sharp 90 bend. Appl Soft Comput 48:563–583
Hager WH (1989) Transitional flow in channel junctions. J Hydraul Eng 115:243–259. https://doi.org/10.1061/(ASCE)0733-9429(1989)115:2(243)
Hsu CC, Wu FS, Lee WJ (1998) Flow at 90° equal-width open-channel junction. J Hydraul Eng 124:186–191. https://doi.org/10.1061/(asce)0733-9429(1998)124:2(186)
Huang J, Weber LJ, Lai YG (2002) Three-dimensional numerical study of flows in open-channel junctions. J Hydraul Eng 128:268–280. https://doi.org/10.1061/(ASCE)0733-9429(2002)128:3(268)
Ivakhnenko A, Muller J (1995) Recent developments of self-organising modeling in prediction and analysis of stock market. Microelectron Reliab 37:1053–1072
Jamali A, Nariman-zadeh N, Darvizeh A, Masoumi A, Hamrang S (2009) Multi-objective evolutionary optimization of polynomial neural networks for modelling and prediction of explosive cutting process. Eng Appl Artif Intell 22:676–687. https://doi.org/10.1016/j.engappai.2008.11.005
Kisi O (2008) The potential of different ANN techniques in evapotranspiration modelling. Hydrol Process 22:2449–2460. https://doi.org/10.1002/hyp.6837
Kisi O, Cigizoglu HK (2007) Comparison of different ANN techniques in river flow prediction. Civ Eng Environ Syst 24:211–231. https://doi.org/10.1080/10286600600888565
Kisi O, Dailr AH, Cimen M, Shiri J (2012) Suspended sediment modeling using genetic programming and soft computing techniques. J Hydrol 450:48–58. https://doi.org/10.1016/j.jhydrol.2012.05.031
Koza JR (1994) Genetic programming as a means for programming computers by natural selection. Stat Comput 4:87–112. https://doi.org/10.1007/BF00175355
Modi PN, Ariel PD, Dandekar MM (1981) Conformal mapping for channel junction flow. J Hydraulics Div 107:1713–1733
Najafzadeh M, Lim SY (2015) Application of improved neuro-fuzzy GMDH to predict scour depth at sluice gates. Earth Sci Inf 8:187–196
Najafzadeh M, Saberi-Movahed F (2018) GMDH-GEP to predict free span expansion rates below pipelines under waves. Mar Georesour Geotechnol. https://doi.org/10.1080/1064119X.2018.1443355
Najafzadeh M, Barani G-A, Hessami-Kermani M-R (2014) Group method of data handling to predict scour at downstream of a ski-jump bucket spillway. Earth Sci Inf 7:231–248
Najafzadeh M, Balf MR, Rashedi E (2016) Prediction of maximum scour depth around piers with debris accumulation using EPR, MT, and GEP models. J Hydroinform 18:867–884
Najafzadeh M, Saberi-Movahed F, Sarkamaryan S (2017a) NF-GMDH-Based self-organized systems to predict bridge pier scour depth under debris flow effects. Mar Georesour Geotechnol 36:1–14
Najafzadeh M, Tafarojnoruz A, Lim SY (2017b) Prediction of local scour depth downstream of sluice gates using data-driven models ISH. J Hydraul Eng 23:195–202
Narimanzadeh N, Darvizeh A, Jamali A, Moeini A (2005) Evolutionary design of generalized polynomial neural networks for modelling and prediction of explosive forming process. J Mater Process Technol 164:1561–1571. https://doi.org/10.1016/j.jmatprotec.2005.02.020
Onen F (2014) GEP prediction of scour around a side weir in curved channel. J Environ Eng Landsc Manag 22:161–170. https://doi.org/10.3846/16486897.2013.865632
Poggio T, Girosi F (1990) Regularization algorithms for learning that are equivalent to multilayer networks. Science 247:978–982
Sattar AMA, Gharabaghi B (2017) Gene expression programming in Open channel hydraulics open channel hydraulics, river hydraulic structures and fluvial geomorphology: for engineers, geomorphologists and physical geographers. CRC Press, Boca Raton
Shakibainia A, Tabatabai MRM, Zarrati AR (2010) Three-dimensional numerical study of flow structure in channel confluences. Can J Civ Eng 37:772–781. https://doi.org/10.1139/L10-016
Sharifipour M, Bonakdari H, Zaji A (2015a) Impact of the confluence angle on flow field and flowmeter accuracy in open channel junctions. Int J Eng Trans B Appl 28:1145
Sharifipour M, Bonakdari H, Zaji AH, Shamshirband S (2015b) Numerical investigation of flow field and flowmeter accuracy in open-channel junctions. Eng Appl Comput Fluid Mech 9:280–290. https://doi.org/10.1080/19942060.2015.1008963
Sharifipour M, Bonakdari H, Zaji AH (2016) Comparison of genetic programming and radial basis function neural network for open-channel junction velocity field prediction. Neural Comput Appl. https://doi.org/10.1007/s00521-016-2713-x
Shiri J, Kisi O (2011) Comparison of genetic programming with neuro-fuzzy systems for predicting short-term water table depth fluctuations. Comput Geosci 37:1692–1701. https://doi.org/10.1016/j.cageo.2010.11.010
Taylor EH (1944) Flow characteristics at rectangular open-channel junctions. Trans Am Soc Civ Eng 109:893–902
Weber LJ, Schumate ED, Mawer N (2001) Experiments on flow at a 90° open-channel junction. J Hydraul Eng 127:340–350. https://doi.org/10.1061/(asce)0733-9429(2001)127:5(340)
Yang QY, Liu TH, Lu WZ, Wang XK (2013) Numerical simulation of confluence flow in open channel with dynamic meshes techniques. Adv Mech Eng 5:1–10. https://doi.org/10.1155/2013/860431
Zahiri AR, Eghbali P (2012) Gene expression programming for prediction of flow discharge in compound channels. J Civ Eng Urban 2:164–169
Zaji AH, Bonakdari H (2015a) Application of artificial neural network and genetic programming models for stimating the longitudinal velocity field in open channel junctions. Flow Meas Instrum 41:81–89. https://doi.org/10.1016/j.flowmeasinst.2014.10.011
Zaji AH, Bonakdari H (2015b) Efficient methods for prediction of velocity fields in open channel junctions based on the artifical neural network. Eng Appl Comput Fluid Mech 9:220–232. https://doi.org/10.1080/19942060.2015.1004821
Zaji AH, Bonakdari H, Gharabaghi B (2018) Remote sensing satellite data preparation for simulating and forecasting river discharge. IEEE Trans Geosci Remote Sens 56:3432–3441
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Zaji, A.H., Bonakdari, H. Velocity Field Simulation of Open-Channel Junction Using Artificial Intelligence Approaches. Iran J Sci Technol Trans Civ Eng 43 (Suppl 1), 549–560 (2019). https://doi.org/10.1007/s40996-018-0185-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40996-018-0185-1