KSCE Journal of Civil Engineering

, Volume 20, Issue 2, pp 990–996 | Cite as

Discharge prediction of circular and rectangular side orifices using artificial neural networks

Water Engineering
First Online:
Received:
Accepted:

Abstract

A side orifice created in the side of a channel is a structure for diverting some of the flow from the main channel for different purposes. The prediction of the discharge through this side structure is very important in hydraulic and irrigation engineering. In the present study, three artificial neural network models including feed forward back propagation, radial basis function, and Generalized Regression neural networks as well as a multiple non-linear regression method were used to predict the discharge coefficient for flow through both square and circular shapes of sharp-crested side orifices. The discharge coefficient was modeled as a function of five input non-dimensional variables resulted from five dimensional variables, which were the type of orifice shape, the diameter or width of the orifice, crest height, depth and velocity of approach flow. The results obtained in this study indicated that all of the neural network models could successfully predict the discharge coefficient with adequate accuracy. However, according to different performance measures, the accuracy of radial basis function approach was a bit better than two other neural network models. The neural network models predicted the discharge coefficient more accurately than the non-linear regression relation.

Keywords

side orifice discharge coefficient prediction artificial neural network flow 
This is a preview of subscription content, log in to check access.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aghayari, F., Honar, T., and Keshavarzi, A. (2009). “A study of spatial variation of discharge coefficient in broad-crested inclined side weirs.” Journal of Irrigation Drainage Engineering, Vol. 58, No. 2, pp. 246–54, DOI:  10.1002/ird.416.CrossRefGoogle Scholar
  2. Benning, R. M., Becker, T. M., and Delgado, A. (2001). “Initial studies of predicting flow fields with an ANN hybrid.” Advances in Engineering Software, Vol. 32, No. 12, pp. 895–901, DOI:  10.1016/S0965-9978(01)00043-6.CrossRefMATHGoogle Scholar
  3. Bilhan, O., Emiroglu, M. E., and Kisi, Ö. (2010). “Application of two different neural network techniques to lateral outflow over rectangular side weirs located on a straight channel.” Advances in Engineering Software, Vol. 41, No. 6, pp. 831–837, DOI:  10.1016/j.advengsoft.2010.03.001.CrossRefMATHGoogle Scholar
  4. Bilhan, O., Emiroglu, M. E., and Kisi, Ö. (2011). “Use of artificial neural networks for prediction of discharge coefficient of triangular labyrinth side weir in curved channels.” Advances in Engineering Software, Vol. 42, No. 4, pp. 208–214, DOI:  10.1016/j.advengsoft.2011.02.006.CrossRefMATHGoogle Scholar
  5. Borghei, M., Jalili, M. R., and Ghodsian, M. (1999). “Discharge coefficient for sharp crested side weir in subcritical flow.” Journal of Hydraulic Engineering, Vol. 125, No. 10, pp. 1051–1056, DOI:  10.1061/(ASCE)0733-9429(1999)125:10(1051).CrossRefGoogle Scholar
  6. Bryant, D. B., Khan, A. A., and Aziz, N. M. (2008). “Investigation of flow upstream of orifices.” Journal of Hydraulic Engineering, Vol. 134, No. 1, pp. 98–104, DOI:  10.1061/(ASCE)0733-9429(2009)135:2(158).CrossRefGoogle Scholar
  7. Cosar, A. and Agaccioglu, H. (2004). “Discharge coefficient of a triangular side weir located on a curved channel.” Journal of Irrigation Drainage Engineering, Vol. 130, No. 5, pp. 321–33, DOI:  10.1061/(ASCE)0733-9437(2004)130:5(410).CrossRefGoogle Scholar
  8. Doszkocs, T. E., Reggia, J., and Lin, X. (1990). “Connectionist models and information retrieval.” Annual Review of Information Science and Technology, Vol. 25, pp. 209–260.Google Scholar
  9. Emiroglu, M., Kaya, N., and Agaccioglu, H. (2010). “Discharge capacity of labyrinth side weir located on a straight channel.” Journal of Irrigation Drainage Engineering, Vol. 136, No. 1, pp. 37–46. DOI:  10.1061/(ASCE)IR.1943-4774.0000112.CrossRefGoogle Scholar
  10. Emiroglu, M. E., Bilhan, O., and Kisi, Ö. (2011). “Neural networks for estimation of discharge capacity of triangular labyrinth side-weir located on a straight channel.” Expert Systems with Applications, Vol. 38, No. 1, pp. 867–874, DOI:  10.1016/j.eswa.2010.07.058.CrossRefGoogle Scholar
  11. Gill, M. A. (1987). “Flow through side slots.” Journal of Environmental Engineering, Vol. 113, No. 5, pp. 1047–1057, DOI:  10.1061/(ASCE)0733-9372(1987)113:5(1047).CrossRefGoogle Scholar
  12. Haykin, S. (1999). Neural networks: A comprehensive foundation (2nd ed.) Upper Saddle Rever, New Jersey: Prentice Hall.MATHGoogle Scholar
  13. Hussain, A., Ahmad, Z., and Asawa, G. L. (2010). “Discharge characteristics of sharp-crested circular side orifices in open channels.” Journal of Flow Measurement and Instrumentation, Vol. 21, No. 3, pp. 418–424, DOI:  10.1016/j.flowmeasinst.2010.06.005.CrossRefGoogle Scholar
  14. Hussain, A., Ahmad, Z., and Asawa, G. L. (2011). “Flow through sharpcrested rectangular side orifices under free flow condition in open channels.” Agricultural Water Management, Vol. 98, No. 10, pp. 1536–1544, DOI:  10.1016/j.agwat.2011.05.004.CrossRefGoogle Scholar
  15. Karami, H., Ardeshir, A., Saneie, M., and Salamatian, S. A. (2012). “Prediction of time variation of scour depth around spur dikes using neural networks.” Journal of Hydroinformatics, Vol. 14, No. 1, pp. 180–191, DOI:  10.2166/hydro.2011.106.CrossRefGoogle Scholar
  16. Keshavarzi, A., Gazni, R., and Homayoon, S. R. (2012). “Prediction of scouring around an arch-shaped bed sill using Neuro-Fuzzy model.” Applied Soft Computing, Vol. 12, No. 1, pp. 486–493, DOI:  10.1016/j.asoc.2011.08.019.CrossRefGoogle Scholar
  17. Kisi, Ö. E., miroglu, M. E., Bilhan, O., and Güven, A. (2012). “Prediction of lateral outflow over triangular labyrinth side weirs under subcritical conditions using soft computing approaches.” Expert Systems with Applications, Vol. 39, No. 3, pp. 3454–3460, DOI:  10.1016/j.eswa.2011.09.035.CrossRefGoogle Scholar
  18. Kocabas, F., Ünal, S., and Ünal, B. (2008). “A neural network approach for prediction of critical submergence of an intake in still water and open channel flow for permeable and impermeable bottom.” Computers & Fluids, Vol. 37, No. 8, pp. 1040–1046, DOI:  10.1016/j.compfluid.2007.11.002.CrossRefMATHGoogle Scholar
  19. Naseri, M. and Othman, F. (2012). “Determination of the length of hydraulic jumps using artificial neural networks.” Advances in Engineering Software, Vol. 48, pp. 27–31, DOI:  10.1016/j.advengsoft.2012.01.003.CrossRefGoogle Scholar
  20. Ojha, C. S. P. and Subbaiah, D. (1997). “Analysis of flow through lateral slot.” Journal of Irrigation Drainage Engineering, Vol. 123, No. 5, pp. 402–405, DOI:  10.1061/(ASCE)0733-9437(1997)123:5(402).CrossRefGoogle Scholar
  21. Pinar, E., Paydas, K., Seckin, G., Akilli, H., Sahin, B., Cobaner, M., Kocaman, S., and Akar, A. (2010). “Artificial neural network approaches for prediction of backwater through arched bridge constrictions.” Advances in Engineering Software, Vol. 41, No. 4, pp. 627–635. DOI:  10.1016/j.advengsoft.2009.12.003.CrossRefMATHGoogle Scholar
  22. Ramamurthy, A. S., Udoyara, S. T., and Serraf, S. (1986). “Rectangular lateral orifices in open channel.” Journal of Environmental Engineering, Vol. 112, No. 2, pp. 292–300, DOI:  10.1061/(ASCE)0733-9372(1986)112:2(292).CrossRefGoogle Scholar
  23. Ramamurthy, A. S., Udoyara, S. T., and Rao, M. V. J. (1987). “Weir orifice units for uniform flow distribution.” Journal of Environmental Engineering, Vol. 113, No. 1, pp. 155–166, DOI:  10.1061/(ASCE)0733-9372(1987)113:1(155).CrossRefGoogle Scholar
  24. Riahi-Madvar, H., Ayyoubzadeh, S. A., and Gholizadeh Atani, M. (2011). “Developing an expert system for predicting alluvial channel geometry using ANN.” Expert Systems with Applications, Vol. 38, No. 1, pp. 215–222, DOI:  10.1016/j.eswa.2010.06.047.CrossRefGoogle Scholar
  25. Seckin, G., Akoz, M. S., Cobaner, M., and Haktanir, T. (2009). “Application of ANN techniques for estimating backwater through bridge constrictions in Mississippi River basin.” Advances in EngineeringSoftware, Vol. 40, No. 10, pp. 1039–1046, DOI:  10.1016/j.advengsoft.2009.03.002.MATHGoogle Scholar
  26. Specht, D. F. (1991). “A general regression neural network.” IEEE Transactions on Neural Networks, Vol. 2, No. 6, pp. 568–576, DOI:  10.1109/72.97934.CrossRefGoogle Scholar
  27. Swamee, P. K., Santosh, K. P., and Masoud, S. A. (1994). “Side weir analysis using elementary discharge coefficient.” Journal of Irrigation Drainage Engineering, Vol. 120, No. 4, pp. 742–55, 10.1061/ (ASCE)0733-9437(1994)120:4(742).CrossRefGoogle Scholar
  28. Ünal, B., Mamak, M., Seckin, G., and Cobaner, M. (2010). “Comparison of an ANN approach with 1-D and 2-D methods for estimating discharge capacity of straight compound channels.” Advances in Engineering Software, Vol. 41, No. 2, pp. 120–129, DOI:  10.1016/j.advengsoft.2009.10.002.CrossRefGoogle Scholar
  29. Yu, H., Xie, T., Paszezynski, S., and Wilamowski, B. M. (2011). “Advantages of radial basis function networks for dynamics system design.” IEEE Transactions on Industrial Electronics, Vol. 58, No. 12, pp. 5438–5450, DOI:  10.1109/TIE.2011.2164773.CrossRefGoogle Scholar
  30. Yuhong, Z. and Wenxin, H. (2009). “Application of artificial neural network to predict the friction factor of open channel flow.” Communications in Nonlinear Science and Numerical Simulation, Vol. 14, No. 5, pp. 2373–2378, DOI:  10.1016/j.cnsns.2008.06.020.CrossRefGoogle Scholar

Copyright information

© Korean Society of Civil Engineers and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Dept. of Civil Engineering, Faculty of EngineeringRazi University & Water and Wastewater Research Center, Tagh-E-BostanKermanshahIran
  2. 2.Dept. of Electrical Engineering, Kermanshah BranchIslamic Azad UniversityKermanshahIran
  3. 3.Kurdistan Agricultural and Natural Resources Research CenterAREEOSanandajIran

Personalised recommendations