Application of Adaptive Neuro-Fuzzy Inference System to Estimate Alongshore Sediment Transport Rate (A Real Case Study: Southern Shorelines of Caspian Sea)

Document Type : Regular Article


1 Department of Marine Physics, Faculty of Marine Sciences, Tarbiat Modares University, Tehran, Iran

2 Ph.D. of Civil Engineering, Advisor of Chairman and Head of Applied Research Group at Water Authority, Khorasan Razavi Water Authority, Mashhad, Iran


Many empirical models have been introduced by scientists during the recent decades for estimating longshore sediment transport rate, but these approaches have been calibrated and applied under limited conditions of the bed profile and specific range of the bed sediment size. The existing empirical relations are linear or exponential regressions based on the observation and measurements data, and there’s a great potential to build more accurate models to predict sediment transport phenomena using soft computation approach. This paper presents a novel case study application of the adaptive Neuro-fuzzy inference system (ANFIS) as a superior modeling technique for estimation of the longshore sediment transport rate in the southern shorelines of the Caspian Sea. The results will be compared with the top three popular existing empirical equations. Daily grab samples from four stations were collected from March 2012 through June 2012. The trained ANFIS model outperformed the existing regression-type empirical equations for the estimation of the alongshore sediment transport rate due to the adaptive structure of the ANFIS model to better fit complex systems.


Google Scholar


Main Subjects

[1]     Bijker EW. Longshore Transport Computations. J Waterw Harb Coast Eng Div 1971;97:687–701.
[2]     Shore Protection Manual, U.S. Army Coastal Engineering Research Center, Department of the Army, Corps of Engineers, U.S. Govt. Printing Office, Washington, DC, USA 1984;1 and 2.
[3]     Kraus NC, Gingerich KJ, Rosati JD. Duck85 surf zone sand transport experiment, Technical report C.E.R.C 89-5, Department of the army waterways experiment station of engineer’s, Vicksburg, Mississippi. 1989.
[4]     Walton Jr TL, Bruno RO. Longshore transport at a detached breakwater, phase II. J Coast Res 1989;5:679–91.
[5]     Kamphuis JW. Alongshore Sediment Transport Rate. J Waterw Port, Coastal, Ocean Eng 1991;117:624–40. doi:10.1061/(ASCE)0733-950X(1991)117:6(624).
[6]     Watanabe A. Total Rate and Distribution of Longshore Sand Transport. Coast. Eng. 1992, New York, NY: American Society of Civil Engineers; 1993, p. 2528–41. doi:10.1061/9780872629332.193.
[7]     Bayram A, Larson M, Miller HC, Kraus NC. Cross-shore distribution of longshore sediment transport: comparison between predictive formulas and field measurements. Coast Eng 2001;44:79–99. doi:10.1016/S0378-3839(01)00023-0.
[8]     Rijn LC van. Longshore sand transport, In: Coastal engineering. Proc. 28th Coast. Eng. Conf. Rest. (VA ) ASCE, WORLD SCIENTIFIC; 2002, p. 2439–2451.
[9]     Sanil Kumar V, Anand N., Chandramohan P, Naik G. Longshore sediment transport rate—measurement and estimation, central west coast of India. Coast Eng 2003;48:95–109. doi:10.1016/S0378-3839(02)00172-2.
[10]    Bakhtyar R, Ghaheri A, Yeganeh-Bakhtiary A, Jeng D-S. Cross-shore sediment transport estimation using fuzzy inference system in the swash zone. J Franklin Inst 2011;348:2005–25. doi:10.1016/j.jfranklin.2011.05.016.
[11]    Kumar VS, Shanas PR, Dora GU, Glejin J, Philip S. Longshore sediment transport in the surf zone based on different formulae: a case study along the central west coast of India. J Coast Conserv 2017;21:1–13. doi:10.1007/s11852-016-0462-8.
[12]    Sadeghifar T, Azarmsa SA, Vafakhah M. Prediction of Alongshore Sediment Transport Rate Using Semi-Empirical Formulas and an Artificial Neural Networks (ANNs) model in Noor Coastal zone. Int J Marit Technol 2013;9:77–86.
[13]    Kabiri-Samani AR, Aghaee-Tarazjani J, Borghei SM, Jeng DS. Application of neural networks and fuzzy logic models to long-shore sediment transport. Appl Soft Comput 2011;11:2880–7. doi:10.1016/j.asoc.2010.11.021.
[14]    Mafi S, Yeganeh-Bakhtiary A, Kazeminezhad MH. Prediction formula for longshore sediment transport rate with M5’ algorithm. J Coast Res 2013;165:2149–54. doi:10.2112/SI65-363.1.
[15]    Seymour RJ, Sessions MH, Castel D. Automated Remote Recording and Analysis of Coastal Data. J Waterw Port, Coastal, Ocean Eng 1985;111:388–400. doi:10.1061/(ASCE)0733-950X(1985)111:2(388).
[16]    Bakhtyar R, Ghaheri A, Yeganeh-Bakhtiary A, Baldock TE. Longshore sediment transport estimation using a fuzzy inference system. Appl Ocean Res 2008;30:273–86. doi:10.1016/j.apor.2008.12.001.
[17]    Bakhtyar R, Barry DA, Li L, Jeng DS, Yeganeh-Bakhtiary A. Modeling sediment transport in the swash zone: A review. Ocean Eng 2009;36:767–83. doi:10.1016/j.oceaneng.2009.03.003.
[18]    Güner HAA, Yüksel Y, Çevik EÖ. Longshore Sediment Transport—Field Data and Estimations Using Neural Networks, Numerical Model, and Empirical Models. J Coast Res 2013;287:311–24. doi:10.2112/JCOASTRES-D-11-00074.1.
[19]    Arı Güner HA, Yumuk HA. Application of a fuzzy inference system for the prediction of longshore sediment transport. Appl Ocean Res 2014;48:162–75. doi:10.1016/j.apor.2014.08.008.
[20]    Robertson B, Gharabaghi B, Hall K. Prediction of Incipient Breaking Wave-Heights Using Artificial Neural Networks and Empirical Relationships. Coast Eng J 2015;57:1550018-1-1550018–27. doi:10.1142/S0578563415500187.
[21]    Sadeghifar T, Nouri Motlagh M, Torabi Azad M, Mohammad Mahdizadeh M. Coastal Wave Height Prediction using Recurrent Neural Networks (RNNs) in the South Caspian Sea. Mar Geod 2017;40:454–65. doi:10.1080/01490419.2017.1359220.
[22]    Thomas LJ, Seabergh WC. Littoral Environment Observations. U. S. Army Eng. Waterw. Exp. Station. Coast. Eng. Res. Cent. 3909 Halls Fenny Road, Viiurg. Missiiippi, 1997, p. 39180–6lS9.
[23]    Kraus NC, Nakashima L. Field method for rapidly determining the dry weight of wet sand samples. J Sediment Petrol 1986;56:550–1.
[24]    Kamphuis JW, Davies MH, Nairn RB, Sayao OJ. Calculation of littoral sand transport rate. Coast Eng 1986;10:1–21. doi:10.1016/0378-3839(86)90036-0.
[25]    J. R. L. A. An Approach to the Sediment Transport Problem from General Physics. By R. A. Bagnold. U.S. Geological Survey Professional Paper 422-I, pp. v + 37, with 15 figs, and 1 table. U.S. Government Printing Office, Washington, D.C., 1966. Price 35 cents. Geol Mag 1967;104:409. doi:10.1017/S0016756800049074.
[26]    Komar PD, Inman DL. Longshore sand transport on beaches. J Geophys Res 1970;75:5914–27. doi:10.1029/JC075i030p05914.
[27]    Dean RG, Dalrymple RA. Coastal Processes with Engineering Applications. Cambridge: Cambridge University Press; 2001. doi:10.1017/CBO9780511754500.
[28]    Longuet-Higgins MS. Longshore currents generated by obliquely incident sea waves: 1. J Geophys Res 1970;75:6778–89. doi:10.1029/JC075i033p06778.
[29]    Martin Larsen P. Industrial applications of fuzzy logic control. Int J Man Mach Stud 1980;12:3–10. doi:10.1016/S0020-7373(80)80050-2.
[30]    Singh AK, Deo MC, Kumar VS. Prediction of littoral drift with artificial neural networks. Hydrol Earth Syst Sci Discuss Eur Geosci Union 2008;12:267–75.
[31]    Wang P, Ebersole BA, Smith ER, Johnson BD. Temporal and spatial variations of surf-zone currents and suspended sediment concentration. Coast Eng 2002;46:175–211. doi:10.1016/S0378-3839(02)00091-1.
[32]    Alizadeh MJ, Shahheydari H, Kavianpour MR, Shamloo H, Barati R. Prediction of longitudinal dispersion coefficient in natural rivers using a cluster-based Bayesian network. Environ Earth Sci 2017;76:86. doi:10.1007/s12665-016-6379-6.
[33]    Barati R, Neyshabouri SAAS, Ahmadi G. Development of empirical models with high accuracy for estimation of drag coefficient of flow around a smooth sphere: An evolutionary approach. Powder Technol 2014;257:11–9. doi:10.1016/j.powtec.2014.02.045.
[34]    Hosseini K, Nodoushan EJ, Barati R, Shahheydari H. Optimal design of labyrinth spillways using meta-heuristic algorithms. KSCE J Civ Eng 2016;20:468–77. doi:10.1007/s12205-015-0462-5.
[35]    Barati R, Neyshabouri SS, Ahmadi G. Sphere drag revisited using shuffled complex evolution algorithm. River flow, 2014, p. 345–53.
[36]    Barati R. Application of excel solver for parameter estimation of the nonlinear Muskingum models. KSCE J Civ Eng 2013;17:1139–48. doi:10.1007/s12205-013-0037-2.
[37]    Barati R. Parameter Estimation of Nonlinear Muskingum Models Using Nelder-Mead Simplex Algorithm. J Hydrol Eng 2011;16:946–54. doi:10.1061/(ASCE)HE.1943-5584.0000379.
[38]    Barati R, Neyshabouri SAAS, Ahmadi G. Issues in Eulerian–Lagrangian modeling of sediment transport under saltation regime. Int J Sediment Res 2018;33:441–61. doi:10.1016/j.ijsrc.2018.04.003.