Olyaie, E., Heydari, M., Banejad, H., Chau, K. (2018). A laboratory investigation on the potential of computational intelligence approaches to estimate the discharge coefficient of piano key weir. Journal of Rehabilitation in Civil Engineering, (), 1-20. doi: 10.22075/jrce.2018.13233.1241

Ehsan Olyaie; Majid Heydari; Hossein Banejad; Kwok-Wing Chau. "A laboratory investigation on the potential of computational intelligence approaches to estimate the discharge coefficient of piano key weir". Journal of Rehabilitation in Civil Engineering, , , 2018, 1-20. doi: 10.22075/jrce.2018.13233.1241

Olyaie, E., Heydari, M., Banejad, H., Chau, K. (2018). 'A laboratory investigation on the potential of computational intelligence approaches to estimate the discharge coefficient of piano key weir', Journal of Rehabilitation in Civil Engineering, (), pp. 1-20. doi: 10.22075/jrce.2018.13233.1241

Olyaie, E., Heydari, M., Banejad, H., Chau, K. A laboratory investigation on the potential of computational intelligence approaches to estimate the discharge coefficient of piano key weir. Journal of Rehabilitation in Civil Engineering, 2018; (): 1-20. doi: 10.22075/jrce.2018.13233.1241

A laboratory investigation on the potential of computational intelligence approaches to estimate the discharge coefficient of piano key weir

^{1}Water Engineering, Agriculture faculty, Bu-Ali Sina University, Iran

^{2}Water Engineering Department, Faculty of Agriculture, Ferdowsi University, Mashhad, Iran

^{3}Department of Civil Department, Environmental Engineering The Hong Kong Polytechnic University Hunghom, Kowloon Hong Kong

Receive Date: 03 December 2017,
Revise Date: 04 January 2018,
Accept Date: 05 February 2018

Abstract

The piano key weir (PKW) is a type of nonlinear control structure that can be used to increase unit discharge over linear overflow weir geometries, particularly when the weir footprint area is restricted To predict the outflow passing over a piano key weir, the discharge coefficient in the general equation of weir needs to be known. This paper presents the results of laboratory model testing of a piano key weir located on the straight open channel flume in the hydraulic laboratory of Bu-Ali Sina University. The discharge coefficient of piano key weir is estimated by using four computational intelligence approaches, namely, feed forward back-propagation neural network (FFBPN), an extension of genetic programming namely gene-expression programming (GEP), least square support vector machine (LSSVM) and extreme learning machine (ELM). For this purpose, 70 laboratory test results were used for determining discharge coefficient of piano key weir for a wide range of discharge values. Coefficient of determination (R2), Nash-Sutcliffe efficiency coefficient (NS), root mean square error (RMSE), mean absolute relative error (MARE), scatter index (SI) and BIAS are used for measuring the models’ performance. Overall performance of the models shows that, all the studied models are able to estimate discharge coefficient of piano key weir satisfactorily. Comparison of results showed that the ELM (R2=0.997 and NS= 0.986) and LSSVM (RMSE=0.016 and MARE=0.027) models were able to produce better results than the other models investigated and could be employed successfully in modeling discharge coefficient from the available experimental data.

[1] Anderson, R.M. (2011). “Piano key weir head discharge relationships.” M.S. thesis, Utah State University, Logan, UT.

[2] Emami, S., Arvanaghi, H., Parsa, J. (2018). “Numerical Investigation of Geometric Parameters Effect of the Labyrinth Weir on the Discharge Coefficient” J. Rehab. Civil Eng. Vol. 6, pp. 1-9.

[3] Ahmadi, M.A., Pouladi, B., Javvi, Y., Alfkhani, S., Soleimani, R. (2015). “Connectionist technique estimates H2S solubility in ionic liquids through a low parameter approach.” J Supercrit Fluids, Vol. 97, pp. 81–87.

[4] Kabiri-Samani, A., Javaheri, A. (2012). “Discharge coefficients for free and submerged flow over Piano Key weirs.” J. Hydraulic Res. Vol. 50, pp. 114-120.

[5] Ribeiro, M.L., Bieri, M., Boillat, J.L., Schleiss, A.J., Singhal, G., Sharma, N. (2012). “Discharge Capacity of Piano Key Weirs.” J Hydraul Eng, Vol.138, pp.199-203.

[6] Du, S., Zhang, J., Deng, Z., Li, J. (2014). “A novel deformation prediction model for mine slope surface using meteorological factors based on kernel extreme learning machine.” Int. J. Eng. Res. Africa, Vol. 12, pp. 67-81.

[7] Dursun, O.F., Kaya, N., Firat, M. (2012). “Estimating discharge coefficient of semi-elliptical side weir using ANFIS.” J. Hydrol. pp. 426-427, pp. 55–62.

[8] Baghalian, S., Bonakdari, H., Nazari, F., Fazli, M. (2012). “Closed-form solution for flow field in curved channels in comparison with experimental and numerical analyses and Artificial Neural Network.” Eng. Appl. Comput. Fluid Mech. Vol. 6, pp. 514-526.

[9] Bilhan, O., Emiroglu, M.E., 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. Vol. 41, Issue 6, pp. 831-837.

[10] Emiroglu, M.E., Kisi, O. (2013). “Prediction of discharge coefficient for trapezoidal labyrinth side weir using a neuro-fuzzy approach.” Water Resour. Manage, Vol. 27, pp. 1473-1488.

[11] Ebtehaj, I., Bonakdari, H. (2016). “Bed load sediment transport estimation in a clean pipe using multilayer perceptron with different training algorithms.” KSCE J. Civil Eng. Vol. 20, pp. 581-589.

[12] Ebtehaj, I., Bonakdari, H., Zaji, A.H., Azimi, H., Khoshbin, F. (2015). “GMDH-type neural network approach for modeling the discharge coefficient of rectangular sharp-crested side weirs.” Eng. Sci. Technol. Intern. J. Vol. 4, pp. 746-757.

[13] Salmasi, F., Yıldırım, G., Masoodi, A., Parsamehr, P. (2013). “Predicting discharge coefficient of compound broad-crested weir by using genetic programming (GP) and artificial neural network (ANN) techniques.” Arab. J. Geosci, Vol 6, pp. 2709-2717.

[14] Khoshbin, F., Bonakdari, H., Ashraf, Talesh, S.H., Ebtehaj, I., Zaji, A.H., Azimi, H. (2016). “Adaptive neuro-fuzzy inference system multi-objective optimization using the genetic algorithm/singular value decomposition method for modelling the discharge coefficient in rectangular sharp-crested side weirs.” Eng. Optim. Vol. 48, pp.933-948.

[15] Zaji, A.H., Bonakdari, H., Shamshirband, S., Qasem, S.N. (2015). “Potential of particle swarm optimization based radial basis function network to predict the discharge coefficient of a modified triangular side weir.” Vol. 45, pp. 404-407.

[16] Kisi, O., Emiroglu, M.E., Bilhan, O., Guven, A. (2012). “Prediction of lateral outflow over triangular labyrinth side weirs under subcritical conditions using soft computing approaches.” Expert Syst Appl, Vol 39, Issue 3, pp. 3454-3460.

[17] Ebtehaj, I., Bonakdari, H. (2017). "No-deposition Sediment Transport in Sewers Using of Gene Expression Programming". Soft Computing in Civil Engineering, Vol 1(1): pp. 29-53. doi: 10.22115/scce.2017.46845

[18] Azimi, H., Bonakdari, H., Ebtehaj, I. (2017). “A Highly Efficient Gene Expression Programming Model for Predicting the Discharge Coefficient in a Side Weir along a Trapezoidal Canal.” Irrig. Drain. Vol. 66(4), pp.655–666.

[19] Huang, G.B., Zhu, Q.Y., Siew, C.K. (2004). “Extreme learning machine: a new learning scheme of feed forward neural networks.” Neural Networks, Vol. 2, pp. 985-990.

[20] Huang, G.B., Zhu, Q.Y., Siew, C.K. (2006). “Extreme learning machine: theory and applications.” Neurocomputing, Vol. 70, Issue 1, pp. 489–501. http://dx.doi.org/ 10.1016/j.neucom.2005.12.126.

[21] Ebtehaj, I., Bonakdari, H., Shamshirband, S. (2016). “Extreme learning machine assessment for estimating sediment transport in open channels.” Engineering with Computers. Vol. 32, pp.691-704.

[23] Jain, A., Ormsbee, L.E. (2002) “Evaluation of short-term water demand forecast modeling techniques: conventional methods versus AI.” J Am Water Works Assoc Vol. 94, pp. 64-72.

[24] Jain, A., Varshney, A.K., Joshi, U.C. (2001). “Short-term water demand forecast modeling at IIT Kanpur using artificial neural networks.” Water Resour Manag, Vol. 15, pp. 299-321.

[25] Laugier, F. (2007). “Design and construction of the first piano key weir (PKW) spillway at the Goulours dam.” The Int J Hydropower and Dams, Vol. 5, pp.94-101.

[26] Laugier, F., Lochu, A., Gille, C., Leite, Ribeiro. M., Boillat, J.L. (2009). “Design and construction of a labyrinth PKW spillway at St-Marc dam, France.” The Int. J. Hydropower and Dams, Vol 5, pp.100-107.

[27] Huang, G.B., Wang, D.H., Lan, Y. (2011). “Extreme learning machines: a survey.” Int J Machine Learn Cybernet, Vol. 2, pp. 107-122.

[28] Naderpour, H., Rafiean, A. H.,Fakharian, P. (2018). "Compressive strength prediction of environmentally friendly concrete using artificial neural networks". Journal of Building Engineering. Vol. 16, pp. 213-219, doi: 10.1016/j.jobe.2018.01.007

[29]Negnevitsky, M. (2005) “Artificial Intelligence: A Guide to Intelligent Systems”, Pearson Education.

[30]Rajurkar, M.P., Kothyarib, U.C., Chaube, U.C. (2004). “Modeling of the daily rainfall-runoff relationship with artificial neural network.” J. Hydrol. Vol. Vol. 285, pp. 96-113.

[31] Sadri, S., Burn, D.H. (2012). “Nonparametric methods for drought severity estimation at ungauged sites." Water Resour Res 48(12): W12505.

[32] Day, P., Das, A.K. (2016), “A utilization of GEP (gene expression programming) metamodel and PSO (particle swarm optimization) tool to predict and optimize the forced convection around a cylinder.” Energy. Vol.95, pp.447-458.

[33] Ferreira, C. (2001). “Gene expression programming: a new adaptive algorithm for solving problems.” Complex Syst, Vol. 13, pp. 87-129.

[34] Ferreira, C. (2002) “Gene expression programming in problem solving, in soft computing and industry.” Springer. Pp. 635-653.

[35] Serre, D. (2002). Matrices: “Theory and Applications.” Springer, Berlin.

[36] Shu, C., Ouarda, T.B.M.J. (2008) “Regional flood frequency analysis at ungauged sites using the adaptive neuro-fuzzy inference system.” J Hydrol, Vol. 349, pp. 31-43.

[37] Ertugrul, O.F. (2016). “Forecasting electricity load by a novel recurrent extreme learning machines approach.” Elec. Power Energy Syst. Vol. 78 pp. 429-435.

[38] Suykens, J. (2001). “Support vector machines: a nonlinear modeling and control perspective.” Europ J Control, Vol. 7, pp. 311-327.

[39] Toro, C.H.F., Meire, S.G., Gálvez, J.F., Fdez-Riverola, F. (2013). “A hybrid artificial intelligence model for river flow forecasting.” Appl Soft Comput, Vol.13, pp. 3449-3458.

[40] Fletcher, D., Goss, E. (1993). “Forecasting with neural networks: an application using bankruptcy data.” Inform Manag, Vol. 24, pp. 159-167.