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Neural Computing and Applications

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25-11-2016 | Original Article

Comparison of genetic programming and radial basis function neural network for open-channel junction velocity field prediction

Authors: Minoo Sharifipour, Hossein Bonakdari, Amir Hossein Zaji

Published in: Neural Computing and Applications | Issue 3/2018

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Abstract

The ability to accurately predict the velocity field in open-channel junctions has a significant impact on the designing process of many hydraulic structures, such as irrigation and drainage channels and sewer networks. The gene expression programming (GEP) and radial basis function neural network (RBF-NN) methods are developed in order to find a continuous spatial velocity description using discrete experimental measurements. By using the coordinates of each point and the tributary-to-main-discharge ratio of an open-channel junction, seven different input combinations are investigated. To find the optimum GEP model, various mathematical and linking functions are studied. The RBF-NN models are developed with various numbers of hidden layer nodes and amounts of spread. A comparison of the results indicates that both GEP and RBF-NN methods can accurately simulate flow in junctions. However, the GEP with root-mean-squared error (RMSE) of 0.2361 performs better than RBF-NN with RMSE of 0.2590. Owing to the higher accuracy and explicit output equation of the GEP, this method can be used in practical situations of predicting open-channel characteristics.

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Bradbrook KF, Biron PM, Lane SN, Richards KS, Roy AG (1998) Investigation of controls on secondary circulation in a simple confluence geometry using a three-dimensional numerical model. Hydrol Process 12(8):1371–1396CrossRef

Ab. Ghani A, Md. Azamathulla H (2010) Gene-expression programming for sediment transport in sewer pipe systems. J Pipeline Syst Eng Pract 2(3):102–106CrossRef

Kisi O, Dailr AH, Cimen M, Shiri J (2012) Suspended sediment modeling using genetic programming and soft computing techniques. J Hydrol 450:48–58CrossRef

Taylor EH (1944) Flow characteristics at rectangular open-channel junction. J Hydraul Eng 109:893–902

Carballada B (1979) Combining flow in open-channels and blockage phenomenon. Internal report RSH 43-79-49, Hydraulic Division, Hydro-Quebec, Montreal

Best J, Reid I (1984) Separation zone at open-channel junctions. J Hydraul Eng 110(11):1588–1594CrossRef

Rhoads BL, Kenworthy ST (1995) Flow structure at an asymmetrical stream confluence. Geomorphology 11(4):273–293CrossRef

Hsu C, Wu F, Lee W (1998) Flow at 90° equal-width open-channel junction. J Hydraul Eng 124(2):186–191CrossRef

Weber LJ, Schumate ED, Mawer N (2001) Experiments on flow at a 90° open-channel junction. J Hydraul Eng 127(5):340–350CrossRef

10.

Huang J, Weber LJ, Lai YG (2002) Three-dimensional numerical study of flows in open-channel junctions. J Hydraul Eng 128(3):268–280CrossRef

11.

Shakibainia A, Tabatabai MRM, Zarrati AR (2010) Three-dimensional numerical study of flow structure in channel confluences. Can J Civil Eng 37(5):772–781CrossRef

12.

Yang Q, Liu T, Lu W, Wang X (2013) Numerical simulation of confluence flow in open-channel with dynamic meshes techniques. Adv Mech Eng. doi:10.1155/2013/860431

13.

Mamak M, Seckin G, Cobaner M, Kisi O (2009) Bridge afflux analysis through arched bridge constrictions using artificial intelligence methods. Civ Eng Environ Syst 26(3):279–293CrossRef

14.

Seckin G, Akoz MS, Cobaner M, Haktanir T (2009) Application of ANN techniques for estimating backwater through bridge constrictions in Mississippi River basin. Adv Eng Softw 40(10):1039–1046CrossRefMATH

15.

Pinar E, Paydas K, Seckin G, Akilli H, Sahin B, Cobaner M, Kocaman S, Atakan Akar M (2010) Artificial neural network approaches for prediction of backwater through arched bridge constrictions. Adv Eng Softw 41(4):627–635CrossRefMATH

16.

Wolfs V, Willems P (2014) Development of discharge-stage curves affected by hysteresis using time varying models, model trees and neural networks. Environ Modell Softw 55:107–119CrossRef

17.

Guven A, Aytek A, Azamathulla HM (2013) A practical approach to formulate stage-discharge relationship in natural rivers. Neural Comput Appl 23(3–4):873–880CrossRef

18.

Kisi O (2008) Constructing neural network sediment estimation models using a data-driven algorithm. Math Comput Simul 79(1):94–103CrossRefMATH

19.

Senthil Kumar A, Ojha C, Goyal MK, Singh R, Swamee P (2011) Modeling of suspended sediment concentration at Kasol in India using ANN, fuzzy Logic, and decision tree algorithms. J Hydrol Eng 17(3):394–404CrossRef

20.

Singh A, Imtiyaz M, Isaac R, Denis D (2012) Comparison of artificial neural network models for sediment yield prediction at single gauging station of watershed in eastern India. J Hydrol Eng 18(1):115–120CrossRef

21.

Goyal MK (2014) Modeling of sediment yield prediction using M5 model tree algorithm and wavelet regression. Water Resour Manag 28(7):1991–2003CrossRef

22.

Singh A, Imtiyaz M, Isaac R, Denis D (2014) Assessing the performance and uncertainty analysis of the SWAT and RBNN models for simulation of sediment yield in the Nagwa watershed, India. Hydrol Sci J 59(2):351–364CrossRef

23.

Fuladipanah M, Sangi E (2014) Neural network for estimation of scour hole dimensions downstream of siphon spillway. Adv Environ Biol 7(13):4254–4264

24.

Azmathullah HM, Deo MC, Deolalikar PB (2005) Neural networks for estimation of scour downstream of a ski-jump bucket. J Hydraul Eng 131(10):898–908CrossRef

25.

Krishna B, Satyaji Rao Y, Vijaya T (2008) Modelling groundwater levels in an urban coastal aquifer using artificial neural networks. Hydrol Process 22(8):1180–1188CrossRef

26.

Rakhshandehroo GR, Vaghefi M, Aghbolaghi MA (2012) Forecasting groundwater level in Shiraz Plain using artificial neural networks. Arab J Sci Eng 37(7):1871–1883CrossRef

27.

Kisi O (2009) Modeling monthly evaporation using two different neural computing techniques. Irrig Sci 27(5):417–430CrossRef

28.

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(6):831–837CrossRefMATH

29.

Cobaner M, Unal B, Kisi O (2009) Suspended sediment concentration estimation by an adaptive neuro-fuzzy and neural network approaches using hydro-meteorological data. J Hydrol 367(1–2):52–61CrossRef

30.

Guven A, Gunal M (2008) Genetic programming approach for prediction of local scour downstream of hydraulic structures. J Irrig Drain 134(2):241–249CrossRef

31.

Azamathulla HM (2012) Gene expression programming for prediction of scour depth downstream of sills. J Hydrol 460:156–159CrossRef

32.

Mohammadpour R, Ghani AA, Azamathulla HM (2013) Estimation of dimension and time variation of local scour at short abutment. Int J River Basin Manag 11(1):121–135CrossRef

33.

Guven A, Azamathulla HM (2012) Gene-expression programming for flip-bucket spillway scour. Water Sci Technol 65(11):1982–1987CrossRef

34.

Azamathulla HM, Deo MC, Deolalikar PB (2008) Alternative neural networks to estimate the scour below spillways. Adv Eng Softw 39(8):689–698CrossRef

35.

Azmathullah HM, Deo MC, Deolalikar PB (2006) Estimation of scour below spillways using neural networks. J Hydraul Res 44(1):61–69CrossRef

36.

Kisi O, Emin Emiroglu M, Bilhan O, Guven A (2012) Prediction of lateral outflow over triangular labyrinth side weirs under subcritical conditions using soft computing approaches. Expert Syst Appl 39(3):3454–3460CrossRef

37.

Unsal M (2012) GEP modeling of penetration depth in sharp crested Weirs. Arab J Sci Eng 37(8):2163–2174CrossRef

38.

Onen F (2014) GEP prediction of scour around a side weir in curved channel. J Environ Eng Landsc 22(3):161–170CrossRef

39.

Md. Azamathulla H (2013) Gene-expression programming to predict friction factor for Southern Italian rivers. Neural Comput Appl 23(5):1421–1426CrossRef

40.

Azamathulla HM, Jarrett RD (2013) Use of gene-expression programming to estimate manning’s roughness coefficient for high gradient streams. Water Resour Manag 27(3):715–729CrossRef

41.

Terzi O, Erol Keskin M (2005) Evaporation estimation using gene expression programming. J Appl Sci 5:508–512CrossRef

42.

Shiri J, Kisi O (2011) Application of artificial intelligence to estimate daily pan evaporation using available and estimated climatic data in the Khozestan Province (South Western Iran). J Irrig Drain 137(7):412–425CrossRef

43.

Azamathulla H, Ghani A, Leow C, Chang C, Zakaria N (2011) Gene-expression programming for the development of a stage-discharge curve of the Pahang river. Water Resour Manag 25(11):2901–2916CrossRef

44.

Zaji AH, Bonakdari H (2015) Efficient methods for prediction of velocity fields in open-channel junctions based on the artificial neural network. Eng Appl Comput Fluid. doi:10.1080/19942060.2015.1004821

45.

Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. MIT press, CambridgeMATH

46.

Ferreira C (2002) Gene expression programming in problem solving. In: Roy R, Köppen M, Ovaska S, Furuhashi T, Hoffmann F (eds) Soft computing and industry. Springer, London, pp 635–653CrossRef

47.

Ferreira C (2001) Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst 13:87–129MathSciNetMATH

48.

Broomhead DS, Lowe D (1988) Radial basis functions, multi-variable functional interpolation and adaptive networks. DTIC Document

49.

Moody J, Darken CJ (1989) Fast learning in networks of locally-tuned processing units. Neural Comput 1(2):281–294CrossRef

50.

Poggio T, Girosi F (1990) Regularization algorithms for learning that are equivalent to multilayer networks. Science 247(4945):978–982MathSciNetCrossRefMATH

51.

Kisi O, Yuksel I, Dogan E (2008) Modelling daily suspended sediment of rivers in Turkey using several data-driven techniques/Modélisation de la charge journalière en matières en suspension dans des rivières turques à l’aide de plusieurs techniques empiriques. Hydrol Sci J 53(6):1270–1285CrossRef

52.

Ay M, Kisi O (2011) Modeling of dissolved oxygen concentration using different neural network techniques in Foundation Creek, El Paso County, Colorado. J Environ Eng 138(6):654–662CrossRef

53.

Pulido-Calvo I, Gutierrez-Estrada JC (2009) Improved irrigation water demand forecasting using a soft-computing hybrid model. Biosyst Eng 102(2):202–218CrossRef

54.

Pulido-Calvo I, Portela MM (2007) Application of neural approaches to one-step daily flow forecasting in Portuguese watersheds. J Hydrol 332(1):1–15CrossRef

55.

Aytek A, Kisi O (2008) A genetic programming approach to suspended sediment modelling. J Hydrol 351(3):288–298CrossRef

56.

Shiri J, Kisi O (2011) Comparison of genetic programming with neuro-fuzzy systems for predicting short-term water table depth fluctuations. Comput Geosci 37(10):1692–1701CrossRef

Title: Comparison of genetic programming and radial basis function neural network for open-channel junction velocity field prediction
Authors: Minoo Sharifipour
Hossein Bonakdari
Amir Hossein Zaji
Publication date: 25-11-2016
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 3/2018
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-016-2713-x

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