Abstract
In general, total roughness coefficient in open channels includes both grain resistance and bedform resistance. Due to the nonlinearity of the roughness coefficient, an accurate prediction of the bedform roughness is difficult. In this study, the capability of artificial neural network and multilayer perceptron (MLP) with firefly algorithm (MLP-FFA) were assessed in predicting the form resistance in channels with dune bedform. In this regard, different input combinations based on flow, bedform, and sediment characteristics were developed in order to determine the best combination. Five different experimental data series were applied to train and test the models. It was found that in predicting the form resistance, the model which took the advantages of both flow and sediment characteristics yielded to better outcomes. It was observed that the bedform characteristics led to an improvement in models accuracy. The results of the sensitivity analysis showed that the Reynolds number and the relative discharge were more effective parameters in the modeling process. Also, investigating the dune geometry (i.e., relative dune height) showed that the densimetric Froude number was the most significant variable.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abo-Hammour Z, Abu Arqub OA, Momani S, Shawagfeh N (2014) Optimization solution of Troesch’s and Bratu’s problems of ordinary type using novel continuous genetic algorithm. Discrete Dyn Nat Soc 2014:1–15
Alvarez EM (1990) The influence of cohesion on sediment movement in channels of circular cross-section (PhD thesis). Department of Civil Engineering, University of Newcastle upon Tyne, England
Arqub OA, Abo-Hammour Z (2014) Numerical solution of systems of second-order boundary value problems using continuous genetic algorithm. Inf Sci 279:396–415
Chang CK, Azamathulla HM, Zakaria NA, Ghani AA (2012) Appraisal of soft computing techniques in prediction of total bed material load in tropical rivers. J Earth Syst Sci 121(1):125–133
Chien N, Wan Z (1999) Mechanics of sediment transport. ASCE Press, Reston
Delafrouz H, Ghaheri A, Ghorbani MA (2018) A novel hybrid neural network based on phase space reconstruction technique for daily river flow prediction. Soft Comput 22(7):2205–2215
Einstein HA (1950) The bed-load function for sediment transportation in open channel flows, vol 1026. US Department of Agriculture, Washington DC
Engel P, Lau YL (1980) Friction factor for two-dimensional dune roughness. J Hydraul Res 18(3):213–225
Engelund F, Hansen E (1967) A monograph on sediment transport in alluvial streams. Technical University of Denmark 0stervoldgade 10, Copenhage K
Gilbert GK (1914) The transport of debris by running water, Professional paper 86, U.S. Geological Survey
Guy HP, Simons DB, Richardson EV (1966) Summary of alluvial channel data from flume experiments, 1956–61 (No. 462-I), US Government Printing Office
Hassanzadeh T, Faez K, Seyfi G (2012) A speech recognition system based on structure equivalent fuzzy neural network trained by firefly algorithm in biomedical engineering (ICoBE). In: 2nd International conference, IEEE
Haykin S, Cybenko G (1999) Approximation by superposition of a sigmoidal function neural networks, 2nd edn. Prentice Hall, Englewood Cliffs, pp 303–314
Heydari H, Zarrati AR, Karimaee Tabarestani M (2014) Bed form characteristics in a live bed alluvial channel. Sci Iran Trans A Civ Eng 21(6):1773–1780
Kaushik A, Tayal DK, Yadav K, Kaur A (2016) Integrating firefly algorithm in artificial neural network models for accurate software cost predictions. J Softw Evol Process 28:665–688
Kavousi-Fard A, Samet H, Marzbani F (2014) A new hybrid modified firefly algorithm and support vector regression model for accurate short term load forecasting. Expert Syst Appl 41(13):6047–6056
Kayarvizhy N, Kanmani S, Uthariaraj R (2014) ANN models optimized using swarm intelligence algorithms. WSEAS Trans Comput 13(45):501–519
Kazemipour AK, Apelt CJ (1983) Effects of irregularity of form on energy losses in open channel flow. Aust Civ Eng Trans CE25:294–299
Kennedy JF (1963) The mechanics of dunes and antidunes in erodible bed channels. J Fluid Mech 16:521–544
Khan MS, Coulibaly P (2006) Application of support vector machine in lake water level prediction. J Hydrol Eng 11(3):199–205
Liriano SL, Day RA (2001) Prediction of scour depth at culvert outlets using neural networks. J Hydroinform 3(4):231–238
Meyer-Peter E, Müller R (1948) Formulas for bed-load transport. In: Proceedings of 2nd meeting IAHR, Stockholm, pp 39–64
Morvan H, Knight D, Wright N, Tang X, Crossley A (2008) The concept of roughness in fluvial hydraulics and its formulation in 1D, 2D and 3D numerical simulation models. J Hydraul Res 46(2):191–208
Ramezani F, Nikoo M (2015) Artificial neural network weights optimization based on social-based algorithm to realize sediment over the river. Soft Comput 19(2):375–387
Rouse H (1965) Critical analysis of open-channel resistance. Hydraul Div J Am Soc Civ Eng 91(4):1–25
Roushangar K (2010) Open channel flow resistance (PhD thesis). Department of Civil Engineering, University of Tabriz, Iran
Roushangar K, Ghasempour R (2017a) Estimation of bedload discharge in sewer pipes with different boundary conditions using evolutionary algorithm. Int J Sediment Res 32(4):564–574
Roushangar K, Ghasempour R (2017b) Prediction of non-cohesive sediment transport in circular channels in deposition and limit of deposition states using SVM. Water Sci Technol Water Supply 17(2):537–551
Saghebian SM (2018) Study on flow resistance coefficient variation of movable beds (PhD thesis). Department of Civil Engineering, University of Tabriz, Iran
Salmasi F (2010) An artificial neural network (ANN) for hydraulics of flows on stepped chutes. Eur J Sci Res 45(3):450–457
Samui P, Dixon B (2011) Application of support vector machine and relevance vector machine to determine evaporative losses in reservoirs. Hydrol Process 9:1361–1369
Soleymani SA et al (2016) A novel method to water level prediction using RBF and FFA. Water Resour Manag 30(9):3265–3283
Sun AY, Wang D, Xu X (2014) Monthly streamflow forecasting using gaussian process regression. J Hydrol 511:72–81
Talebbeydokhti N, Hekmatzadeh AA, Rakhshandehroo GR (2006) Experimental modeling of dune bedform in a sand bed channels. Iran J Sci Technol 30(4):503–516
United States Army Corps of Engineers, US (1935) Waterways experiment station, Vicksburg, Mississippi. Studies of river bed materials and their movement with special reference to the lower Mississippi River, Paper 17, 1935A, 161 pp
Van der Mark CF, Blom A, Hulscher SJ (2008) Quantification of variability in bedform geometry. Geophys Res 113:1–11
Williams GP (1970) Flume width and water depth effects in sediment transport experiments. US Geological Survey, Professional Paper 562-H
Wong KW, Wong PM, Gedeon TD, Fung CC (2003) Rainfall prediction model using soft computing technique. Soft Comput 7(6):434–438
Yang XS (2010) Firefly algorithm, stochastic test functions and design optimization. Int J Bioinspir Comput 2(2):78–84
Yang GC, Kwok CY, Sobral YD (2018) The effects of bed form roughness on total suspended load via the Lattice Boltzmann Method. Appl Math Model 63:591–610
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Ethical approval
This article does not contain any studies with human participants or animals performed by any of the authors.
Additional information
Communicated by V. Loia.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Roushangar, K., Saghebian, S.M., Kirca, V.S.O. et al. Prediction of form roughness coefficient in alluvial channels using efficient hybrid approaches. Soft Comput 24, 18531–18543 (2020). https://doi.org/10.1007/s00500-020-05090-5
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-020-05090-5