Energy Dissipation Prediction for Stepped Spillway Based on Genetic Algorithm–Support Vector Regression
This article has a reply.
VIEW THE REPLYThis article has a reply.
VIEW THE REPLYThis article has a reply.
VIEW THE REPLYPublication: Journal of Irrigation and Drainage Engineering
Volume 144, Issue 4
Abstract
Accurately forecasting energy dissipation is critical to the hydraulic design of stepped spillways. In this study, support vector machine regression (SVR) was applied to estimate the energy dissipation of a stepped spillway. To develop an accurate model, a genetic algorithm (GA) was employed to determine the SVR parameters, including the penalty parameter , insensitive loss coefficient , and kernel constant . Four dimensionless parameters that influence the energy dissipation of stepped spillways, including the relative critical flow depth, drop number, number of steps, and spillway slope, were selected as the input variables in the GA-SVR model. Overall, 216 experimental data points (collected from the literature) were used for energy dissipation prediction. The predicted values of relative energy dissipation yielded root-mean-square error (RMSE), squared correlation coefficient (), and mean relative error (MRD) values of 7.1859, 0.9540, and 0.1197, respectively, for the testing data set. Moreover, a back-propagation neural network (BPNN) was developed using the same data set. A detailed comparison of the results indicated that GA-SVR performed better than the traditional BPNN model in predicting the energy dissipation of the stepped spillway; thus, based on these results, the GA-SVR model can be successfully used to predict the energy dissipation of stepped spillways.
Get full access to this article
View all available purchase options and get full access to this article.
References
Azamathulla, H. M. (2012). “Gene expression programming for prediction of scour depth downstream of sills.” J. Hydrol., 460–461(5), 156–159.
Azmathullah, H. M., Deo, M. C., and Deolalikar, P. B. (2005). “Neural networks for estimation of scour downstream of a ski-jump bucket.” J. Hydraul. Eng., 898–908.
Azmathullah, H. M., Deo, M. C., and Deolalikar, P. B. (2006). “Estimation of scour below spillways using neural networks.” J. Hydral. Res., 44(1), 61–69.
Bagatur, T., and Onen, F. (2014). “A predictive model on air entrainment by plunging water jets using GEP and ANN.” KSCE J. Civ. Eng., 18(1), 304–314.
Baylar, A., Hanbay, D., and Batan, M. (2009). “Application of least square support vector machines in the prediction of aeration performance of plunging overfall jets from weirs.” Expert Syst. Appl., 36(4), 8368–8374.
Baylar, A., Hanbay, D., and Ozpolat, E. (2007). “Modeling aeration efficiency of stepped cascades by using ANFIS.” CLEAN-Soil Air Water, 35(2), 186–192.
Behzad, M., Asghari, K., Eazi, M., and Palhang, M. (2009). “Generalization performance of support vector machines and neural networks in runoff modeling.” Expert Syst. Appl., 36(4), 7624–7629.
Boes, R. M., and Hager, W. H. (2003). “Hydraulic design of stepped spillways.” J. Hydraul. Eng., 671–679.
Chamani, M. R., and Rajaratnam, N. (1994). “Jet flow on stepped spillways.” J. Hydraul. Eng., 254–259.
Chamani, M. R., and Rajaratnam, N. (1999). “Characteristics of skimming flow over stepped spillways.” J. Hydraul. Eng., 361–368.
Chang, C.-C., and Lin, C.-J. (2011). “LIBSVM: A library for support vector machines.” ⟨http://www.csie.ntu.edu.tw/∼cjlin/libsvm⟩ (Jan. 1, 2017).
Chanson, H. (1993). “Stepped spillway flows and air entrainment.” Can. J. Civil Eng., 20(3), 422–435.
Chanson, H. (1994a). “Comparison of energy dissipation between nappe and skimming flow regimes on stepped chutes.” J. Hydral. Res., 32(2), 213–218.
Chanson, H. (1994b). “Hydraulics of skimming flows over stepped channels and spillways.” J. Hydral. Res., 32(3), 445–460.
Chanson, H. (1995). “Jet flow on stepped spillways.” J. Hydraul. Eng., 441–448.
Chanson, H. (1996). “Prediction of the transition nappe/skimming flow on a stepped channel.” J. Hydral. Res., 34(3), 421–429.
Chinnarasri, C., and Wongwises, S. (2006). “Flow patterns and energy dissipation over various stepped chutes.” J. Irrig. Drain. Eng., 70–76.
Cortes, C., and Vapnik, V. (1995). “Support-vector networks.” Mach. Learn., 20(3), 273–297.
Deka, P. C. (2014). “Support vector machine applications in the field of hydrology: A review.” Appl. Software Comput., 19(6), 372–386.
Dibike, Y. B., Velickov, S., Solomatine, D., and Abbott, M. B. (2001). “Model induction with support vector machines: Introduction and applications.” J. Comput. Civ. Eng., 208–216.
Dursun, O. F., Kaya, N., and Firat, M. (2012). “Estimating discharge coefficient of semi-elliptical side weir using ANFIS.” J. Hydrol., 426–427, 55–62.
Emiroglu, M. E., Bilhan, O., and Kisi, O. (2011). “Neural networks for estimation of discharge capacity of triangular labyrinth side-weir located on a straight channel.” Expert Syst. Appl., 38(1), 867–874.
Emiroglu, M. E., Kaya, N., and Agaccioglu, H. (2009). “Discharge capacity of labyrinth side weir located on a straight channel.” J. Irrig. Drain. Eng., 37–46.
Emiroglu, M. E., Kisi, O., and Bilhan, O. (2010). “Predicting discharge capacity of triangular labyrinth side weir located on a straight channel by using an adaptive neuro-fuzzy technique.” Adv. Eng. Software, 41(2), 154–160.
Felder, S., Guenther, P., and Chanson, H. (2012). “Air-water flow properties and energy dissipation on stepped spillways: A physical study of several pooled stepped configurations.”, School of Civil Engineering, Univ. of Queensland, Brisbane, Australia.
Granata, F., Gargano, R., and Marinis, G. D. (2016). “Support vector regression for rainfall-runoff modeling in urban drainage: A comparison with the EPA’s storm water management model.” Water, 8(3), 69.
Granata, F., Papirio, S., Esposito, G., Gargano, R., and de Marinis, G. (2017). “Machine learning algorithms for the forecasting of wastewater quality indicators.” Water, 9(2), 105.
Hanbay, D., Baylar, A., and Batan, M. (2009). “Prediction of aeration efficiency on stepped cascades by using least square support vector machines.” Expert Syst. Appl., 36(3), 4248–4252.
Harianto, T., and Hughes, R. (2007). “Hydraulics of stepped chutes: The transition flow.” J. Hydral. Res., 45(1), 140–141.
Holland, J. (1975). Adaptation in natural and artificial systems, University of Michigan Press, Ann Arbor, MI.
Hong, J.-H., Goyal, M. K., Chiew, Y.-M., and Chua, L. H. (2012). “Predicting time-dependent pier scour depth with support vector regression.” J. Hydrol., 468(22), 241–248.
Khatibi, R., Salmasi, F., Ghorbani, M. A., and Asadi, H. (2014). “Modelling energy dissipation over stepped-gabion weirs by artificial intelligence.” Water Resour. Manage., 28(7), 1807–1821.
Laucelli, D., and Giustolisi, O. (2011). “Scour depth modelling by a multi-objective evolutionary paradigm.” Environ. Model. Software, 26(4), 498–509.
MATLAB [Computer software]. MathWorks, Natick, MA.
Mohandes, M. A., Halawani, T. O., Rehman, S., and Hussain, A. A. (2004). “Support vector machines for wind speed prediction.” Renew. Energy, 29(6), 939–947.
Najafzadeh, M., Etemad-Shahidi, A., and Lim, S. Y. (2016). “Scour prediction in long contractions using ANFIS and SVM.” Ocean Eng., 111(6), 128–135.
Pal, M., and Goel, A. (2006). “Prediction of the end-depth ratio and discharge in semi-circular and circular shaped channels using support vector machines.” Flow Meas. Instrum., 17(1), 49–57.
Parsaie, A., Haghiabi, A. H., Saneie, M., and Torabi, H. (2016). “Prediction of energy dissipation on the stepped spillway using the multivariate adaptive regression splines.” ISH J. Hydraul. Eng., 22(3), 281–292.
Peyras, L. A., Royet, P., and Degoutte, G. (1992). “Flow and energy dissipation over stepped gabion weirs.” J. Hydraul. Eng., 707–717.
Rajaratnam, N. (1990). “Skimming flow in stepped spillways.” J. Hydraul. Eng., 587–591.
Roushangar, K., Akhgar, S., Salmasi, F., and Shiri, J. (2014). “Modeling energy dissipation over stepped spillways using machine learning approaches.” J. Hydrol., 508, 254–265.
Roushangar, K., Akhgar, S., Salmasi, F., and Shiri, J. (2017). “Neural networks-and neuro-fuzzy-based determination of influential parameters on energy dissipation over stepped spillways under nappe flow regime.” ISH J. Hydraul. Eng., 23(1), 57–62.
Salmasi, F., and Özger, M. (2014). “Neuro-fuzzy approach for estimating energy dissipation in skimming flow over stepped spillways.” Arab. J. Sci. Eng., 39(8), 6099–6108.
Sholichin, M., Dermawan, V., Suhardjono, and Krisnayanti, D. S. (2016). “Energy dissipation of skimming flow on flat and pooled stepped spillways.” Aust. J. Basic Appl. Sci., 10(6), 62–68.
Smola, A. J. (2004). “A tutorial on support vector regression.” Stat. Comput., 14(3), 199–222.
Tang, H., Zhang, H., and Xiujuan, M. (2013). “Offshore wind speed load predicting based on GA-SVM.” Proc., Int. Conf. on Information Engineering and Applications (IEA) 2012, Springer, London, 439–447.
Torabi, S., Roostami, R. A., Torabi, S., Boostani, F., and Roushan, A. (2013). “Energy dissipation on stepped spillways with reverse inclination.” Water Eng., 6(17), 53–62.
Vapnik, V. N. (1995). The nature of statistical learning theory, Springer, Berlin.
Yang, L. (2011). “FarutoUltimate: A toolbox with implements for support vector machines based on libsvm.” ⟨http://www.matlabsky.com⟩ (Jan. 16, 2017).
Yu, P. S., Chen, S. T., and Chang, I. F. (2006). “Support vector regression for real-time flood stage forecasting.” J. Hydrol., 328(3–4), 704–716.
Zhou, J., Shi, J., and Li, G. (2011). “Fine tuning support vector machines for short-term wind speed forecasting.” Energy Convers. Manage., 52(4), 1990–1998.
Information & Authors
Information
Published In
Copyright
©2018 American Society of Civil Engineers.
History
Received: Apr 26, 2017
Accepted: Oct 18, 2017
Published online: Feb 2, 2018
Published in print: Apr 1, 2018
Discussion open until: Jul 2, 2018
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.