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Volumn 18, Issue 4, 2015, Pages 746-757

GMDH-type neural network approach for modeling the discharge coefficient of rectangular sharp-crested side weirs

Author keywords

Discharge coefficient; Empirical equations; Froude number; Group method of data handling (GMDH); Side weir

Indexed keywords


EID: 85017342627     PISSN: None     EISSN: 22150986     Source Type: Journal    
DOI: 10.1016/j.jestch.2015.04.012     Document Type: Article
Times cited : (147)

References (60)
  • 1
    • 58149456912 scopus 로고    scopus 로고
    • Constructing optimal educational tests using GMDH-based item ranking and selection
    • [1] Abdel-Aal, R.E., El-Alfy, E.S.M., Constructing optimal educational tests using GMDH-based item ranking and selection. Neurocomputing 72:4 (2009), 1184–1197.
    • (2009) Neurocomputing , vol.72 , Issue.4 , pp. 1184-1197
    • Abdel-Aal, R.E.1    El-Alfy, E.S.M.2
  • 2
    • 84890903535 scopus 로고    scopus 로고
    • Prediction of partition coefficients of alkaloids in ionic liquids based aqueous biphasic systems using hybrid group method of data handling (GMDH) neural network
    • [2] Abdolrahimi, S., Nasernejad, B., Pazuki, G., Prediction of partition coefficients of alkaloids in ionic liquids based aqueous biphasic systems using hybrid group method of data handling (GMDH) neural network. J. Mol. Liq. 191 (2014), 79–84.
    • (2014) J. Mol. Liq. , vol.191 , pp. 79-84
    • Abdolrahimi, S.1    Nasernejad, B.2    Pazuki, G.3
  • 3
    • 49049121001 scopus 로고    scopus 로고
    • Modeling of multiple short-length-scale stall cells in an axial compressor using evolved GMDH neural networks
    • [3] Amanifard, N., Nariman-Zadeh, N., Farahani, M.H., Khalkhali, A., Modeling of multiple short-length-scale stall cells in an axial compressor using evolved GMDH neural networks. Energy Convers. Manag. 49:10 (2008), 2588–2594.
    • (2008) Energy Convers. Manag. , vol.49 , Issue.10 , pp. 2588-2594
    • Amanifard, N.1    Nariman-Zadeh, N.2    Farahani, M.H.3    Khalkhali, A.4
  • 4
    • 12444329746 scopus 로고    scopus 로고
    • Tool life testing in gundrilling: an application of the group method of data handling (GMDH)
    • [4] Astakhov, V.P., Galitsky, V.V., Tool life testing in gundrilling: an application of the group method of data handling (GMDH). Int. J. Mach. Tool Manuf. 45:4 (2005), 509–517.
    • (2005) Int. J. Mach. Tool Manuf. , vol.45 , Issue.4 , pp. 509-517
    • Astakhov, V.P.1    Galitsky, V.V.2
  • 5
    • 84899551190 scopus 로고    scopus 로고
    • Estimation of the viscosity of nine nanofluids using a hybrid GMDH-type neural network system
    • [5] Atashrouz, S., Pazuki, G., Alimoradi, Y., Estimation of the viscosity of nine nanofluids using a hybrid GMDH-type neural network system. Fluid Phase Equilib. 372 (2014), 43–48.
    • (2014) Fluid Phase Equilib. , vol.372 , pp. 43-48
    • Atashrouz, S.1    Pazuki, G.2    Alimoradi, Y.3
  • 6
    • 84893798873 scopus 로고    scopus 로고
    • Discharge coefficient of rectangular sharp-crested side weirs, part II: Domínguez's method
    • [6] Bagheri, S., Kabiri-Samani, A.R., Heidarpour, H., Discharge coefficient of rectangular sharp-crested side weirs, part II: Domínguez's method. Flow Meas. Instrum. 35 (2014), 116–121.
    • (2014) Flow Meas. Instrum. , vol.35 , pp. 116-121
    • Bagheri, S.1    Kabiri-Samani, A.R.2    Heidarpour, H.3
  • 7
    • 80052326951 scopus 로고    scopus 로고
    • Numerical analysis and prediction of the velocity field in curved open channel using artificial neural network and genetic algorithm
    • [7] Bonakdari, H., Baghalian, S., Nazari, F., Fazli, M., Numerical analysis and prediction of the velocity field in curved open channel using artificial neural network and genetic algorithm. Eng. Appl. Comput. Fluid Mech. 5:3 (2011), 384–396.
    • (2011) Eng. Appl. Comput. Fluid Mech. , vol.5 , Issue.3 , pp. 384-396
    • Bonakdari, H.1    Baghalian, S.2    Nazari, F.3    Fazli, M.4
  • 8
    • 0033407434 scopus 로고    scopus 로고
    • Discharge coefficient for sharp-crested side weir in subcritical flow
    • [8] Borghei, S.M., Jalili, M.R., Ghodsian, M., Discharge coefficient for sharp-crested side weir in subcritical flow. J. Hydraul. Eng. 125:10 (1999), 1051–1056.
    • (1999) J. Hydraul. Eng. , vol.125 , Issue.10 , pp. 1051-1056
    • Borghei, S.M.1    Jalili, M.R.2    Ghodsian, M.3
  • 9
    • 0026191769 scopus 로고
    • Discharge coefficient of lateral diversion from trapezoidal channel
    • [9] Cheong, H.F., Discharge coefficient of lateral diversion from trapezoidal channel. J. Irrig. Drain. Eng. 117:4 (1991), 321–333.
    • (1991) J. Irrig. Drain. Eng. , vol.117 , Issue.4 , pp. 321-333
    • Cheong, H.F.1
  • 10
    • 8644220509 scopus 로고    scopus 로고
    • Discharge coefficient of a triangular side-weir located on a curved channel
    • [10] Cosar, A., Agaccioglu, H., Discharge coefficient of a triangular side-weir located on a curved channel. J. Irrig. Drain. Eng. 130:5 (2004), 321–333.
    • (2004) J. Irrig. Drain. Eng. , vol.130 , Issue.5 , pp. 321-333
    • Cosar, A.1    Agaccioglu, H.2
  • 11
    • 0001616709 scopus 로고
    • Saggio di teoria del funzionamento degli stramazzi laterali
    • (in Italian)
    • [11] De Marchi, G., Saggio di teoria del funzionamento degli stramazzi laterali. L'Energia Electr. Milan 11 (1934), 849–860 (in Italian).
    • (1934) L'Energia Electr. Milan , vol.11 , pp. 849-860
    • De Marchi, G.1
  • 12
    • 84857190718 scopus 로고    scopus 로고
    • Estimating discharge coefficient of semi-elliptical side weir using ANFIS
    • [12] Dursun, O.F., Kaya, N., Firat, M., Estimating discharge coefficient of semi-elliptical side weir using ANFIS. J. Hydrol. 426–427 (2012), 55–62.
    • (2012) J. Hydrol. , vol.426-427 , pp. 55-62
    • Dursun, O.F.1    Kaya, N.2    Firat, M.3
  • 13
    • 84882314134 scopus 로고    scopus 로고
    • Evaluation of sediment transport in sewer using artificial neural network
    • [13] Ebtehaj, I., Bonakdari, H., Evaluation of sediment transport in sewer using artificial neural network. Eng. Appl. Comput. Fluid Mech. 7:3 (2013), 382–392.
    • (2013) Eng. Appl. Comput. Fluid Mech. , vol.7 , Issue.3 , pp. 382-392
    • Ebtehaj, I.1    Bonakdari, H.2
  • 14
    • 84918827786 scopus 로고    scopus 로고
    • Comparison of genetic algorithm and imperialist competitive algorithms in predicting bed load transport in clean pipe
    • [14] Ebtehaj, I., Bonakdari, H., Comparison of genetic algorithm and imperialist competitive algorithms in predicting bed load transport in clean pipe. Water Sci. Technol. 70:10 (2014), 1695–1701.
    • (2014) Water Sci. Technol. , vol.70 , Issue.10 , pp. 1695-1701
    • Ebtehaj, I.1    Bonakdari, H.2
  • 15
    • 85027942599 scopus 로고    scopus 로고
    • Performance evaluation of adaptive neural fuzzy inference system for sediment transport in sewers
    • [15] Ebtehaj, I., Bonakdari, H., Performance evaluation of adaptive neural fuzzy inference system for sediment transport in sewers. Water Resour. Manag. 28:13 (2014), 4765–4779.
    • (2014) Water Resour. Manag. , vol.28 , Issue.13 , pp. 4765-4779
    • Ebtehaj, I.1    Bonakdari, H.2
  • 16
    • 84910642446 scopus 로고    scopus 로고
    • Pareto genetic design of GMDH-type neural network for predict discharge coefficient in rectangular side orifices
    • [16] Ebtehaj, I., Bonakdari, H., Khoshbin, F., Azimi, H., Pareto genetic design of GMDH-type neural network for predict discharge coefficient in rectangular side orifices. Flow Meas. Instrum. 41 (2015), 67–74.
    • (2015) Flow Meas. Instrum. , vol.41 , pp. 67-74
    • Ebtehaj, I.1    Bonakdari, H.2    Khoshbin, F.3    Azimi, H.4
  • 18
    • 0141528647 scopus 로고
    • Hydraulics of flow over side weirs
    • (Ph.D. thesis) University of Southampton UK
    • [18] El-Khashab, A.M.M., Hydraulics of flow over side weirs. (Ph.D. thesis), 1975, University of Southampton, UK.
    • (1975)
    • El-Khashab, A.M.M.1
  • 19
    • 79957845100 scopus 로고    scopus 로고
    • Discharging capacity of rectangular side weirs in straight open channels
    • [19] Emiroglu, M.E., Agaccioglu, H., Kaya, N., Discharging capacity of rectangular side weirs in straight open channels. Flow Meas. Instrum. 22:4 (2011), 319–330.
    • (2011) Flow Meas. Instrum. , vol.22 , Issue.4 , pp. 319-330
    • Emiroglu, M.E.1    Agaccioglu, H.2    Kaya, N.3
  • 20
    • 0004096959 scopus 로고
    • Self-organizing Method in Modeling: GMDH Type algorithm
    • Marcel Dekker Inc. New York, USA
    • [20] Farlow, S.J., Self-organizing Method in Modeling: GMDH Type algorithm. 1984, Marcel Dekker Inc., New York, USA.
    • (1984)
    • Farlow, S.J.1
  • 21
    • 71349088571 scopus 로고    scopus 로고
    • The practical research on flood forecasting based on artificial neural networks
    • [21] Feng, L.H., Lu, J., The practical research on flood forecasting based on artificial neural networks. Expert Syst. Appl. 37 (2010), 2974–2977.
    • (2010) Expert Syst. Appl. , vol.37 , pp. 2974-2977
    • Feng, L.H.1    Lu, J.2
  • 22
    • 0001083304 scopus 로고
    • A criterion of efficiency for rainfall–runoff models
    • [22] Garrick, M., Cunnane, C., Nash, J.E., A criterion of efficiency for rainfall–runoff models. J. Hydrol. 36:3 (1978), 375–381.
    • (1978) J. Hydrol. , vol.36 , Issue.3 , pp. 375-381
    • Garrick, M.1    Cunnane, C.2    Nash, J.E.3
  • 23
    • 42949091393 scopus 로고    scopus 로고
    • Supercritical flow over a rectangular side weir
    • [23] Ghodsian, M., Supercritical flow over a rectangular side weir. Can. J. Civil Eng. 30:3 (2003), 596–600.
    • (2003) Can. J. Civil Eng. , vol.30 , Issue.3 , pp. 596-600
    • Ghodsian, M.1
  • 24
    • 20844456071 scopus 로고    scopus 로고
    • Improving generalization of artificial neural networks in rainfall–runoff modeling
    • [24] Giustolisi, O., Laucelli, D., Improving generalization of artificial neural networks in rainfall–runoff modeling. Hydrol. Sci. J. 50:3 (2005), 439–457.
    • (2005) Hydrol. Sci. J. , vol.50 , Issue.3 , pp. 439-457
    • Giustolisi, O.1    Laucelli, D.2
  • 25
    • 0023324548 scopus 로고
    • Lateral outflow over side weirs
    • [25] Hager, W.H., Lateral outflow over side weirs. J. Hydraul. Eng. 113:4 (1987), 491–504.
    • (1987) J. Hydraul. Eng. , vol.113 , Issue.4 , pp. 491-504
    • Hager, W.H.1
  • 26
    • 0029413797 scopus 로고
    • Artificial neural network modeling of the rainfall–runoff process
    • [26] Hsu, K.L., Gupta, H.V., Sorooshian, S., Artificial neural network modeling of the rainfall–runoff process. Water Resour. Res. 31:10 (1995), 2517–2530.
    • (1995) Water Resour. Res. , vol.31 , Issue.10 , pp. 2517-2530
    • Hsu, K.L.1    Gupta, H.V.2    Sorooshian, S.3
  • 27
    • 33748323200 scopus 로고    scopus 로고
    • Fuzzy GMDH-type neural network model and its application to forecasting of mobile communication
    • [27] Hwang, H.S., Fuzzy GMDH-type neural network model and its application to forecasting of mobile communication. Comput. Ind. Eng. 50:4 (2006), 450–457.
    • (2006) Comput. Ind. Eng. , vol.50 , Issue.4 , pp. 450-457
    • Hwang, H.S.1
  • 28
    • 0001987690 scopus 로고
    • A numerical approach to genetic programming for system identification
    • [28] Iba, H., Sato, T., A numerical approach to genetic programming for system identification. Evol. Comput. 3:4 (1995), 417–452.
    • (1995) Evol. Comput. , vol.3 , Issue.4 , pp. 417-452
    • Iba, H.1    Sato, T.2
  • 29
    • 0015142058 scopus 로고
    • Polynomial theory of complex systems
    • [29] Ivakhnenko, A.G., Polynomial theory of complex systems. IEEE Trans. Syst. Man Cybern. SMC-1:4 (1971), 364–378.
    • (1971) IEEE Trans. Syst. Man Cybern. , vol.SMC-1 , Issue.4 , pp. 364-378
    • Ivakhnenko, A.G.1
  • 30
    • 0036640826 scopus 로고    scopus 로고
    • Short-term water demand forecast modeling techniques—conventional methods versus AI
    • [30] Jain, A., Ormsbee, L.E., Short-term water demand forecast modeling techniques—conventional methods versus AI. J. Am. Water Works Assoc. 94 (2002), 64–72.
    • (2002) J. Am. Water Works Assoc. , vol.94 , pp. 64-72
    • Jain, A.1    Ormsbee, L.E.2
  • 31
    • 0035494446 scopus 로고    scopus 로고
    • Short-term water demand forecast modelling at IIT Kanpur using artificial neural networks
    • [31] Jain, A., Varshney, A.K., Joshi, U.C., Short-term water demand forecast modelling at IIT Kanpur using artificial neural networks. Water Resour. Manag. 15:5 (2001), 299–321.
    • (2001) Water Resour. Manag. , vol.15 , Issue.5 , pp. 299-321
    • Jain, A.1    Varshney, A.K.2    Joshi, U.C.3
  • 32
    • 84995350672 scopus 로고    scopus 로고
    • Discussion of ‘Discharge coefficient of rectangular side weir, by R. Singh, D. Manivannan and T. Satyanarayana’
    • [32] Jalili, M.R., Borghei, S.M., Discussion of ‘Discharge coefficient of rectangular side weir, by R. Singh, D. Manivannan and T. Satyanarayana’. J. Irrig. Drain. Eng., 122(2), 1996, 132.
    • (1996) J. Irrig. Drain. Eng. , vol.122 , Issue.2 , pp. 132
    • Jalili, M.R.1    Borghei, S.M.2
  • 33
    • 79961185394 scopus 로고    scopus 로고
    • Pareto robust design of controllers with probabilistic uncertainties using multi objective evolutionary algorithms
    • (Ph.D. thesis) University of Guilan Iran
    • [33] Jamali, A., Pareto robust design of controllers with probabilistic uncertainties using multi objective evolutionary algorithms. (Ph.D. thesis), 2009, University of Guilan, Iran.
    • (2009)
    • Jamali, A.1
  • 34
    • 59349097197 scopus 로고    scopus 로고
    • An investigation on the Su-NSPT correlation using GMDH type neural networks and genetic algorithms
    • [34] Kalantary, F., Ardalan, H., Nariman-Zadeh, N., An investigation on the Su-NSPT correlation using GMDH type neural networks and genetic algorithms. Eng. Geol. 104:1 (2009), 144–155.
    • (2009) Eng. Geol. , vol.104 , Issue.1 , pp. 144-155
    • Kalantary, F.1    Ardalan, H.2    Nariman-Zadeh, N.3
  • 35
    • 78751704571 scopus 로고    scopus 로고
    • Discharge coefficient of semielliptical side weir in subcritical flow
    • [35] Kaya, N., Emiroglu, M.E., Agaccioglu, H., Discharge coefficient of semielliptical side weir in subcritical flow. Flow Meas. Instrum. 22:1 (2011), 25–32.
    • (2011) Flow Meas. Instrum. , vol.22 , Issue.1 , pp. 25-32
    • Kaya, N.1    Emiroglu, M.E.2    Agaccioglu, H.3
  • 36
    • 80255131241 scopus 로고    scopus 로고
    • Prediction of lateral outflow over triangular labyrinth side weirs under subcritical conditions using soft computing approaches
    • [36] Kisi, O., Emiroglu, M.E., Bilhan, O., Guven, A., Prediction of lateral outflow over triangular labyrinth side weirs under subcritical conditions using soft computing approaches. Expert Syst. Appl. 39:3 (2012), 3454–3460.
    • (2012) Expert Syst. Appl. , vol.39 , Issue.3 , pp. 3454-3460
    • Kisi, O.1    Emiroglu, M.E.2    Bilhan, O.3    Guven, A.4
  • 37
    • 33749163568 scopus 로고    scopus 로고
    • Estimation of critical submergence for an intake in a stratified fluid media by neuro-fuzzy approach
    • [37] Kocabas, U., Ulker, S., Estimation of critical submergence for an intake in a stratified fluid media by neuro-fuzzy approach. Environ. Fluid Mech. 6:5 (2006), 489–495.
    • (2006) Environ. Fluid Mech. , vol.6 , Issue.5 , pp. 489-495
    • Kocabas, U.1    Ulker, S.2
  • 38
    • 63549111632 scopus 로고    scopus 로고
    • Revised GMDH-type neural network algorithm with a feedback loop identifying sigmoid function neural network
    • [38] Kondo, T., Ueno, J., Revised GMDH-type neural network algorithm with a feedback loop identifying sigmoid function neural network. Int. J. Innov. Comput. Inform. Control 2:5 (2006), 985–996.
    • (2006) Int. J. Innov. Comput. Inform. Control , vol.2 , Issue.5 , pp. 985-996
    • Kondo, T.1    Ueno, J.2
  • 39
    • 84878710023 scopus 로고    scopus 로고
    • Hybrid multi-layered GMDH-type neural network using principal component-regression analysis and its application to medical image diagnosis of lung cancer
    • IEEE
    • [39] Kondo, T., Ueno, J., Takao, S., Hybrid multi-layered GMDH-type neural network using principal component-regression analysis and its application to medical image diagnosis of lung cancer. Paper Presented on BioMedical Computing ASE/IEEE International Conference on, pp. 20–27, 2012, IEEE.
    • (2012) Paper Presented on BioMedical Computing ASE/IEEE International Conference on, pp. 20–27
    • Kondo, T.1    Ueno, J.2    Takao, S.3
  • 40
    • 0032920124 scopus 로고    scopus 로고
    • Evaluating the use of “goodness of fit” measures in hydrologic and hydroclimatic model validation
    • [40] Legates, D.R., McCabe, G.J., Evaluating the use of “goodness of fit” measures in hydrologic and hydroclimatic model validation. Water Resour. Res. 35:1 (1999), 233–241.
    • (1999) Water Resour. Res. , vol.35 , Issue.1 , pp. 233-241
    • Legates, D.R.1    McCabe, G.J.2
  • 41
    • 84875010202 scopus 로고    scopus 로고
    • Shear wave velocity by polynomial neural networks and genetic algorithms based on geotechnical soil properties
    • [41] Mola-Abasi, H., Eslami, A., Tabatabaei shorijeh, P., Shear wave velocity by polynomial neural networks and genetic algorithms based on geotechnical soil properties. Arab. J. Sci. Eng. 38:4 (2013), 829–838.
    • (2013) Arab. J. Sci. Eng. , vol.38 , Issue.4 , pp. 829-838
    • Mola-Abasi, H.1    Eslami, A.2    Tabatabaei shorijeh, P.3
  • 42
    • 84880059802 scopus 로고    scopus 로고
    • An unscented Kalman filter in designing dynamic GMDH neural networks for robust fault detection
    • [42] Mrugalski, M., An unscented Kalman filter in designing dynamic GMDH neural networks for robust fault detection. Int. J. Appl. Math. Comput. Sci. 23:1 (2013), 157–169.
    • (2013) Int. J. Appl. Math. Comput. Sci. , vol.23 , Issue.1 , pp. 157-169
    • Mrugalski, M.1
  • 43
    • 84875239991 scopus 로고    scopus 로고
    • Aboutment scour in live-bed and clear-water using GMDH network
    • [43] Najafzadeh, M., Barani, G.A., Hessami Kermani, M.R., Aboutment scour in live-bed and clear-water using GMDH network. Water Sci. Technol. 67:5 (2013), 1121–1128.
    • (2013) Water Sci. Technol. , vol.67 , Issue.5 , pp. 1121-1128
    • Najafzadeh, M.1    Barani, G.A.2    Hessami Kermani, M.R.3
  • 44
    • 84893170465 scopus 로고    scopus 로고
    • Application of improved neuro-fuzzy GMDH to predict scour depth at sluice gates
    • [44] Najafzadeh, M., Lim, S.Y., Application of improved neuro-fuzzy GMDH to predict scour depth at sluice gates. Earth Sci. Inform. 8:1 (2015), 187–196.
    • (2015) Earth Sci. Inform. , vol.8 , Issue.1 , pp. 187-196
    • Najafzadeh, M.1    Lim, S.Y.2
  • 45
    • 0037705805 scopus 로고    scopus 로고
    • Hybrid genetic design of GMDH type neural networks using singular value decomposition for modelling and prediction of the explosive cutting process
    • [45] Nariman-Zadeh, N., Darvizeh, A., Ahmad-Zadeh, G.R., Hybrid genetic design of GMDH type neural networks using singular value decomposition for modelling and prediction of the explosive cutting process. Proc. Instn. Mech. Eng. B: J. Eng. Manuf. 217:6 (2003), 779–790.
    • (2003) Proc. Instn. Mech. Eng. B: J. Eng. Manuf. , vol.217 , Issue.6 , pp. 779-790
    • Nariman-Zadeh, N.1    Darvizeh, A.2    Ahmad-Zadeh, G.R.3
  • 46
    • 17844390396 scopus 로고    scopus 로고
    • Evolutionary design of generalized polynomial neural networks for modelling and prediction of explosive forming process
    • [46] Nariman-Zadeh, N., Darvizeh, A., Jamali, A., Moeini, A., Evolutionary design of generalized polynomial neural networks for modelling and prediction of explosive forming process. J. Mater. Process. Technol. 164 (2005), 1561–1571.
    • (2005) J. Mater. Process. Technol. , vol.164 , pp. 1561-1571
    • Nariman-Zadeh, N.1    Darvizeh, A.2    Jamali, A.3    Moeini, A.4
  • 47
    • 61849114625 scopus 로고    scopus 로고
    • Pareto genetic design of GMDH-type neural networks for nonlinear systems
    • Czech Technical University Prague, Czech Republic
    • [47] Nariman-Zadeh, N., Jamali, A., Pareto genetic design of GMDH-type neural networks for nonlinear systems. Proceedings of the International Workshop on Inductive Modelling, 2007, Czech Technical University, Prague, Czech Republic, 96–103.
    • (2007) Proceedings of the International Workshop on Inductive Modelling , pp. 96-103
    • Nariman-Zadeh, N.1    Jamali, A.2
  • 48
    • 0002947932 scopus 로고
    • Discussion of spatially varied flow over side weir
    • [48] Nandesamoorthy, T., Thomson, A., Discussion of spatially varied flow over side weir. J. Hydraul. Div. 98:12 (1972), 2234–2235.
    • (1972) J. Hydraul. Div. , vol.98 , Issue.12 , pp. 2234-2235
    • Nandesamoorthy, T.1    Thomson, A.2
  • 49
    • 0347135926 scopus 로고    scopus 로고
    • Modeling of the daily rainfall–runoff relationship with artificial neural network
    • [49] Rajurkar, M.P., Kothyari, U.C., Chaube, U.C., Modeling of the daily rainfall–runoff relationship with artificial neural network. J. Hydrol. 285:1 (2004), 96–113.
    • (2004) J. Hydrol. , vol.285 , Issue.1 , pp. 96-113
    • Rajurkar, M.P.1    Kothyari, U.C.2    Chaube, U.C.3
  • 51
    • 37649016085 scopus 로고    scopus 로고
    • Study of flow over side weirs under supercritical conditions
    • [51] Rao, K.D., Pillai, C.R.S., Study of flow over side weirs under supercritical conditions. Water Resour. Manag. 22:1 (2008), 131–143.
    • (2008) Water Resour. Manag. , vol.22 , Issue.1 , pp. 131-143
    • Rao, K.D.1    Pillai, C.R.S.2
  • 52
  • 53
    • 55949130863 scopus 로고    scopus 로고
    • Energy demand prediction using GMDH networks
    • [53] Srinivasan, D., Energy demand prediction using GMDH networks. Neurocomputing 72:1 (2008), 625–629.
    • (2008) Neurocomputing , vol.72 , Issue.1 , pp. 625-629
    • Srinivasan, D.1
  • 54
    • 0015281580 scopus 로고
    • Spatially varied flow over side weirs
    • [54] Subramanya, K., Awasthy, S.C., Spatially varied flow over side weirs. J. Hydraul. Div. 98:1 (1972), 1–10.
    • (1972) J. Hydraul. Div. , vol.98 , Issue.1 , pp. 1-10
    • Subramanya, K.1    Awasthy, S.C.2
  • 55
    • 33644614517 scopus 로고    scopus 로고
    • A GMDH neural network-based approach to robust fault diagnosis: application to the DAMADICS benchmark problem
    • [55] Witczak, M., Korbicz, J., Mrugalski, M., Patton, R.J., A GMDH neural network-based approach to robust fault diagnosis: application to the DAMADICS benchmark problem. Control Eng. Pract. 14:6 (2006), 671–683.
    • (2006) Control Eng. Pract. , vol.14 , Issue.6 , pp. 671-683
    • Witczak, M.1    Korbicz, J.2    Mrugalski, M.3    Patton, R.J.4
  • 56
    • 70649104621 scopus 로고    scopus 로고
    • Dynamic classifier ensemble selection based on GMDH
    • [56] Xiao, J., He, C., Dynamic classifier ensemble selection based on GMDH. Comput. Sci. Optim. IEEE 1 (2009), 731–734.
    • (2009) Comput. Sci. Optim. IEEE , vol.1 , pp. 731-734
    • Xiao, J.1    He, C.2
  • 57
    • 0033362601 scopus 로고    scopus 로고
    • Evolving artificial neural networks
    • [57] Yao, X., Evolving artificial neural networks. Proc. IEEE 87:9 (1999), 1423–1447.
    • (1999) Proc. IEEE , vol.87 , Issue.9 , pp. 1423-1447
    • Yao, X.1
  • 58
    • 8644267783 scopus 로고
    • Discussion of spatially varied flow over side weir
    • [58] Yu-Tech, L., Discussion of spatially varied flow over side weir. J. Hydraul. Eng. 98:11 (1972), 2046–2048.
    • (1972) J. Hydraul. Eng. , vol.98 , Issue.11 , pp. 2046-2048
    • Yu-Tech, L.1
  • 59
    • 84930217896 scopus 로고    scopus 로고
    • Efficient methods for prediction of velocity fields in open channel junctions based on the artificial neural network
    • [59] Zaji, A.H., Bonakdari, H., Efficient methods for prediction of velocity fields in open channel junctions based on the artificial neural network. Eng. Appl. Comput. Fluid Mech., 2015, 1–13.
    • (2015) Eng. Appl. Comput. Fluid Mech. , pp. 1-13
    • Zaji, A.H.1    Bonakdari, H.2
  • 60
    • 84855892833 scopus 로고    scopus 로고
    • A D-GMDH model for time series forecasting
    • [60] Zhang, M., He, C., Liatsis, P., A D-GMDH model for time series forecasting. Expert Syst. Appl. 39:5 (2012), 5711–5716.
    • (2012) Expert Syst. Appl. , vol.39 , Issue.5 , pp. 5711-5716
    • Zhang, M.1    He, C.2    Liatsis, P.3


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