Skip to main content
SpringerLink
Log in

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

  • Water Engineering
  • Published:
KSCE Journal of Civil Engineering Aims and scope

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • 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.

    Article  Google Scholar 

  • 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.

    Article  MATH  Google Scholar 

  • 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.

    Article  MATH  Google Scholar 

  • 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.

    Article  MATH  Google Scholar 

  • 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).

    Article  Google Scholar 

  • 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).

    Article  Google Scholar 

  • 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).

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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).

    Article  Google Scholar 

  • Haykin, S. (1999). Neural networks: A comprehensive foundation (2nd ed.) Upper Saddle Rever, New Jersey: Prentice Hall.

    MATH  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  MATH  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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).

    Article  Google Scholar 

  • 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.

    Article  MATH  Google Scholar 

  • 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).

    Article  Google Scholar 

  • 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).

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    MATH  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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).

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dept. of Civil Engineering, Faculty of Engineering, Razi University & Water and Wastewater Research Center, Tagh-E-Bostan, Kermanshah, 67149, Iran

    A. Eghbalzadeh & M. Javan

  2. Dept. of Electrical Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran

    M. Hayati

  3. Kurdistan Agricultural and Natural Resources Research Center, AREEO, Sanandaj, Iran

    A. Amini

Authors
  1. A. Eghbalzadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. Javan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. Hayati
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. A. Amini
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. Eghbalzadeh.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Eghbalzadeh, A., Javan, M., Hayati, M. et al. Discharge prediction of circular and rectangular side orifices using artificial neural networks. KSCE J Civ Eng 20, 990–996 (2016). https://doi.org/10.1007/s12205-015-0440-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12205-015-0440-y

Keywords

Search

Navigation