Comparative analysis of GMDH neural network based on genetic algorithm and particle swarm optimization in stable channel design

Applied Mathematics and Computation

Volumn 313, Issue , 2017, Pages 271-286

  Shaghaghi, Saba ^a   Bonakdari, Hossein ^a   Gholami, Azadeh ^a   Ebtehaj, Isa ^a   Zeinolabedini, Maryam ^b  

^a RAZI UNIVERSITY   (Iran)

^b GRADUATE UNIVERSITY OF ADVANCED TECHNOLOGY   (Iran)

Author keywords

Genetic algorithm; GMDH; Particle swarm optimization; Regression method; Stable channel width

Indexed keywords

DATA HANDLING; DESIGN; FORECASTING; GENETIC ALGORITHMS; LEARNING ALGORITHMS; NEURAL NETWORKS; PARETO PRINCIPLE; REGRESSION ANALYSIS; SEDIMENT TRANSPORT; UNCERTAINTY ANALYSIS;

CORRELATION COEFFICIENT; EROSION AND SEDIMENT TRANSPORTS; GENETIC ALGORITHM AND PARTICLE SWARM OPTIMIZATIONS; GMDH; GROUP METHOD OF DATA HANDLING; REGRESSION METHOD; SENSITIVITY AND UNCERTAINTY ANALYSIS; STABLE CHANNELS;

PARTICLE SWARM OPTIMIZATION (PSO);

EID: 85020943803     PISSN: 00963003     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.amc.2017.06.012     Document Type: Article

References (88)

1. Singh, V.P., on the theories of hydraulic geometry. Int. J. Sediment Res. 18:3 (2003), 196–218.
2. Millar, R.G., Quick, M.C., Effect of bank stability on geometry of gravel rivers. J. Hydraul. Eng. 119:12 (1993), 1343–1363.
3. Kennedy, R.G., The prevention of silting in irrigation canals (including appendix). J. Minutes Proc. Inst. Civil Eng. 119 (1895), 281–290.
4. Cheema, M.N., Mariño, M.A., DeVries, J.J., Stable width of an alluvial channel. J. Irrig. Drain. Eng. 123:1 (1997), 55–61.
5. Anderson, R.J., Bledsoe, B.P., Hession, W.C., Width of streams and rivers in response to vegetation, bank material, and other factors. J. Am. Water Resour. Assoc. 40:5 (2004), 1159–1172.
6. Parker, G., Wilcock, P.R., Paola, C., Dietrich, W.E., Pitlick, J., Physical basis for quasi‐universal relations describing bankfull hydraulic geometry of single‐thread gravel bed rivers. J. Geophys. Res. Earth Surf., 112(F4), 2007, F04005.
7. Lee, J.S., Julien, P.Y., Downstream hydraulic geometry of alluvial channels. J. Hydraul. Eng. 132:12 (2006), 1347–1352.
8. Blench, T., Regime theory for self-formed sediment-bearing channels. Proc. Am. Soc. Civil Eng. 77:5 (1951), 1–18.
9. Leopold, L.B., Maddock, T., The hydraulic geometry of stream channels and some physiographic implications. Prog. Phys. Geogr. 20:1 (1953), 1–57 (No. 252).
10. Kellerhals, R., Stable channels with gravel-paved beds. J. Waterw. Harb. Div. 93:1 (1967), 63–84.
11. Hey, R.D., Thorne, C.R., Stable channels with mobile gravel beds. J. Hydraul. Eng. 112:8 (1986), 671–689.
12. Chang, H.H., Geometry of gravel streams. J. Hydraul. Div. 106:9 (1980), 1443–1456.
13. Julien, P.Y., Wargadalam, J., Alluvial channel geometry: theory and applications. J. Hydraul. Eng. 121:4 (1995), 312–325.
14. Dierks, S., Gustavson, K., Schneider, J., Wilson, S., Stream restoration design using the stable channel analytical model (SAM). Proceedings of Protection and Restoration of Urban and Rural Streams, 2004, 155–164.
15. Teal, M.J., Anderson, P.E., Sediment investigation and stable channel design for the lower mud river. Proceedings of Impacts of Global Climate Change, ASCE, 2005, 1–12.
16. Mehta, D.J., Ramani, M.M., Joshi, M.M., Application of 1-D HEC-RAS model in design of channels. Methodology 1:7 (2013), 4–62.
17. Afzalimehr, H., Abdolhosseini, M., Singh, V.P., Hydraulic geometry relations for stable channel design. J. Hydrol. Eng. 15:10 (2010), 859–864.
18. Davidson, S.K., Hey, R.D., Regime equations for natural meandering cobble-and gravel-bed rivers. J. Hydraul. Eng. 137:9 (2011), 894–910.
19. Abdelhaleem, F.S., Amin, A.M., Ibraheem, M.M., Updated regime equations for alluvial Egyptian canals. Alex. Eng. J. 55:1 (2016), 505–512.
20. Gholami, A., Bonakdari, H., Zaji, A.H., Michelson, D.G., Akhtari, A.A., Improving the performance of multi-layer perceptron and radial basis function models with a decision tree model to predict flow variables in a sharp 90° bend. Appl. Soft Comput. 48 (2016), 563–583.
21. Gholami, A., Bonakdari, H., Zaji, A.H., Ajeel Fenjan, S., Akhtari, A.A., Design of modified structure multi-layer perceptron networks based on decision trees for the prediction of flow parameters in 90° open-channel bends. Eng. Appl. Comput. Fluid Mech. 10:1 (2016), 194–209.
22. Gholami, A., Bonakdari, H., Zaji, A.H., Fenjan, S.A., Akhtari, A.A., New radial basis function network method based on decision trees to predict flow variables in a curved channel. Neural Comput. Appl., 2017, 1–15, 10.1007/s00521-017-2875-1.
23. Gholami, A., Bonakdari, H., Ebtehaj, I., Akhtari, A.A., Design of an adaptive neuro-fuzzy computing technique for predicting flow variables in a 90° sharp bend. J. Hydroinform., 2017, 10.2166/hydro.2017.200 jh2017200.
24. Bonakdari, H., Gholami, A., Evaluation of artificial neural network model and statistical analysis relationships to predict the stable channel width. River Flow, 2016, Taylor & Francis Group, Boca Raton, USA, 417 July 11–14, 2016.
25. Kisi, O., Shiri, J., Karimi, S., Shamshirband, S., Motamedi, S., Petković, D., Hashim, R., A survey of water level fluctuation predicting in Urmia Lake using support vector machine with firefly algorithm. Appl. Math. Comput. 270 (2015), 731–743.
26. Gao, S., Wang, Y., Cheng, J., Inazumi, Y., Tang, Z., Ant colony optimization with clustering for solving the dynamic location routing problem. Appl. Math. Comput. 285 (2016), 149–173.
27. Yin, Y., Shang, P., Forecasting traffic time series with multivariate predicting method. Appl. Math. Comput. 291 (2016), 266–278.
28. Najafzadeh, M., Balf, M.R., Rashedi, E., Prediction of maximum scour depth around piers with debris accumulation using EPR, MT, and GEP models. J. Hydroinform. 18:5 (2016), 867–884.
29. Bonakdari, H., Ebtehaj, I., Verification of equation for non-deposition sediment transport in flood water canals. Proceedings of the 7th International Conference on Fluvial Hydraulics, River Flow, 2014, 1527–1533.
30. Ebtehaj, I., Bonakdari, H., Bed load sediment transport estimation in a clean pipe using multilayer perceptron with different training algorithms. KSCE J. Civil Eng. 20:2 (2016), 581–589.
31. Khadangi, E., Madvar, H.R., Kiani, H., Application of artificial neural networks in establishing regime channel relationships. Proceedings of the 2nd International Conference on Computer, Control and Communication, Feb. 2009, 1–6.
32. Tahershamsi, A., Tabatabai, M.M., Shirkhani, R., An evaluation model of artificial neural network to predict stable width in gravel bed rivers. Int. J. Environ. Sci. Technol. 9:2 (2012), 333–342.
33. 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.
34. Ebtehaj, I., Bonakdari, H., Zaji, A.H., Azimi, H., Khoshbin, F., GMDH-type neural network approach for modeling the discharge coefficient of rectangular sharp-crested side weirs. Eng. Sci. Technol.: Int. J. 18:4 (2015), 746–757.
35. Ebtehaj, I., Bonakdari, H., Evaluation of sediment transport in sewer using artificial neural network. Eng. Appl. Comput. Fluid Mech. 7:3 (2013), 382–392.
36. Najafzadeh, M., Barani, G.A., Hessami Kermani, M.R., Aboutment scour in live-bed and clear-water using GMDH Network. J. Water Sci. Technol. 67:5 (2013), 1121–1128.
37. 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. Inst. Mech. Eng. B: J. Eng. Manuf. 217:6 (2003), 779–790.
38. Najafzadeh, M., Barani, G.A., Comparison of group method of data handling based genetic programming and back propagation systems to predict scour depth around bridge piers. Sci. Iran. 18:6 (2011), 1207–1213.
39. Najafzadeh, M., Azamathulla, H.M., Group method of data handling to predict scour depth around bridge piers. Neural Comput. Appl. 23:7–8 (2013), 2107–2112.
40. Najafzadeh, M., Barani, G.A., Azamathulla, H.M., GMDH to predict scour depth around a pier in cohesive soils. Appl. Ocean Res. 40 (2013), 35–41.
41. Najafzadeh, M., Barani, G.A., Hessami-Kermani, M.R., Group method of data handling to predict scour depth around vertical piles under regular waves. Sci. Iran. 20:3 (2013), 406–413.
42. 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.
43. Nariman-Zadehk, N., Atashkari, K., Jamali, A., Pilechi, A., Yao, X., Inverse modelling of multi-objective thermodynamically optimized turbojet engines using GMDH-type neural networks and evolutionary algorithms. Eng. Optim. 37:5 (2005), 437–462.
44. 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.
45. Gholami, A., Bonakdari, H., Ebtehaj, I., Shaghaghi, S., Khoshbin, F., Developing an expert group method of data handling system for predicting the geometry of a stable channel with a gravel bed. Earth Surf. Process. Landf., 2017, 10.1002/esp.4104.
46. Anastasakis, L., Mort, N., Research report. 2001, University of Sheffield, Department of Automatic Control and Systems Engineering.
47. Ivakhnenko, A.G., Müller, J.A., Recent developments of self organizing modeling in prediction and analysis of stock market. Microelectron. Reliab. 37 (1995), 1053–1072.
48. Farlow, S.J., Self-Organizing Methods in Modeling: GMDH Type Algorithms, 54, 1984, CRC Press.
49. Ivakhnenko, A.G., Polynomial theory of complex systems. IEEE Trans. Syst. Man Cybern. SMC-1:4 (1971), 364–378.
50. Iba, H., Sato, T., A numerical approach to genetic programming for system identification. Evolut. Comput. 3:4 (1995), 417–452.
51. Nariman Zadeh, N., Jamali, A., Pareto genetic design of GMDH -type neural networks for nonlinear systems. Proceedings of International Workshop on Inductive Modeling, 2007, Czech Technical University, Prague, Czech Republic, 96–103.
52. Jamali, A., Nariman-Zadeh, N., Darvizeh, A., Masoumi, A., Hamrang, S., Multi-objective evolutionary optimization of polynomial neural networks for modelling and prediction of explosive cutting process. Eng. Appl. Artif. Intell. 22:4 (2009), 676–687.
53. Kondo, T., Ueno, J., Revised GMDH-type neural network algorithm with a feedback loop identifying sigmoid function neural network. Int. J. Innov. Comput. Inf. Control 2:5 (2006), 985–996.
54. Eberhart, R., Kennedy, J., A new optimizer using particle swarm theory. Proceedings of the IEEE 6th International Symposium on Micro Machine and Human Science, Oct. 1995, 39–43.
55. Kennedy, J., Eberhart, R., Particle swarm optimization. IEEE Int. Conf. Neural Netw. 4 (1995), 1942–1948.
56. P. Angeline, Evolutionary optimization versus particle swarm optimization: philosophy and performance differences, In Evolutionary Programming VII, Springer Berlin/Heidelberg, pp. 601–610.
57. Clerc, M., Kennedy, J., The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. Evol. Comput. 6:1 (2002), 58–73.
58. Parsopoulos, K.E., Vrahatis, M.N., Recent approaches to global optimization problems through particle swarm optimization. Natural Computing I, 2002, Kluwer Academic Publishers, 235–306.
59. Mendes, R., Kennedy, J., Neves, J., The fully informed particle swarm: simpler, maybe better. IEEE Trans. Evol. Comput. 8:3 (2004), 204–210.
60. Shi, Y., Eberhart, R.C., Empirical study of particle swarm optimization. Proceedings of the 1999 Congress on Evolutionary Computation, 1999, IEEE Service Center, Piscataway, NJ, 1945–1950.
61. EL-Zonkoly, A.M., Optimal tuning of power systems stabilizers and AVR gains using particle swarm optimization. Expert Syst. Appl. 31:3 (2006), 551–557.
62. Ebtehaj, I., Bonakdari, H., Assessment of evolutionary algorithms in predicting non-deposition sediment transport. Urban Water J. 13:5 (2016), 499–510.
63. Najafzadeh, M., Barani, G.A., Hessami-Kermani, M.R., Group method of data handling to predict scour at downstream of a ski-jump bucket spillway. Earth Sci. Inform. 7:4 (2014), 231–248.
64. Onwubolu, G.C., Design of hybrid differential evolution and group method of data handling networks for modeling and prediction. Inf. Sci. 178:18 (2008), 3616–3634.
65. Onwubolu, G.C., Sharma, A., Particle swarm optimization for the assignment of facilities to locations. New Optimization Techniques in Engineering, 2004, Springer, Berlin, Heidelberg, 567–584.
66. Ikeda, S., Parker, G., Kimura, Y., Stable width and depth of straight gravel rivers with heterogeneous bed materials. Water Resour. Res. 24:5 (1988), 713–722.
67. Savenije, H.H., The width of a bankfull channel; Lacey's formula explained. J. Hydrol. 276:1 (2003), 176–183.
68. Eaton, B.C., Millar, R.G., Optimal alluvial channel width under a bank stability constraint. Geomorphology 62:1 (2004), 35–45.
69. Yousefi, N., Khodashenas, S.R., Eslamian, S., Askari, Z., Estimating width of the stable channels using multivariable mathematical models. Arab. J. Geosci. 9:4 (2016), 1–11.
70. Andrews, E.D., Bank stability and channel width adjustment, East Fork River, Wyoming. Water Resour. Res. 18:4 (1982), 1184–1192.
71. Stevens, M.A., Width of straight alluvial channels. J. Hydraul. Eng. 115:3 (1989), 309–326.
72. Millar, R.G., Quick, M.C., Stable width and depth of gravel-bed rivers with cohesive banks. J. Hydraul. Eng. 124:10 (1998), 1005–1013.
73. Thorne, C.R., River width adjustment. I: Processes and mechanisms. J. Hydraul. Eng. 124:9 (1998), 881–902.
74. Copeland, R., Soar, P., Thorne, C.R., Channel-forming discharge and hydraulic geometry width predictors in meandering sand-bed rivers. Proceedings of Impacts of Global Climate Change, 2005, 1–12.
75. Lacey, G., Flow in alluvial channels with sandy mobile beds. Proc. Inst. Civil Eng. 9:2 (1958), 145–164.
76. Bray, D.I., Regime equations for gravel-bed Rivers. Gravel-bed Rivers, 1982, John Wiley and Sons, New Jersey, USA, 517–542.
77. Yalin, M.S., River Mechanics. 1992, Pergamon Press, New York, N.Y.
78. Arbeláez, A.C., Posada, L., Regime equations for mountain streams in the Cauca region of Colombia. Proceedings of Hydrology Days Conference, 2007, 177–188.
79. Kondap, D.M., Garde, R.J., Application of optimization principles in the study of stable channels. Proceedings of International Workshop on Alluvial River Problems, 1980, University of Roorkee, Roorkee, India.
80. Lee, J.S., Julien, P.Y., Closure to “Downstream hydraulic geometry of alluvial channels” by J.S. Lee and P.Y. Julien. J. Hydraul. Eng. 134:4 (2008), 504–505.
81. Parker, G., Self-formed straight rivers with equilibrium banks and mobile bed. Part 1. The sand-silt river. J. Fluid Mech. 89:01 (1978), 109–125.
82. Mejia, G., (Doctoral dissertation, Master thesis). 2001, Water Resources Graduate Program. Universidad Nacional de Colombia, Medellín.
83. Xu, J., Comparison of hydraulic geometry between sand‐and Gravel‐bed Rivers in relation to channel pattern discrimination. Earth Surf. Process. Landf. 29:5 (2004), 645–657.
84. Najafzadeh, M., Bonakdari, H., Application of a neuro-fuzzy GMDH model for predicting the velocity at limit of deposition in storm sewers. J. Pipeline Syst. Eng. Pract., 8(1), 2017, 06016003.
85. Najafzadeh, M., Tafarojnoruz, A., Evaluation of Neuro-fuzzy GMDH-based particle swarm optimization to predict longitudinal dispersion coefficient in rivers. Environ. Earth Sci., 75(2), 2016, 157.
86. Sattar, A.M., Prediction of organic micropollutant removal in soil aquifer treatment system using GEP. J. Hydrol. Eng. 29:1 (2016), 1–15 04016027.
87. Sattar, A.M., Gharabaghi, B., McBean, E., Predicting timing of watermain failure using gene expression models for infrastructure planning. Water Resour. Manag. 30 (2016), 1635–1651.
88. Ebtehaj, I., Sattar, A.M., Bonakdari, H., Zaji, A.H., Prediction of scour depth around bridge piers using self-adaptive extreme learning machine. J. Hydroinform. 19:2 (2017), 207–224.