No-Deposition Sediment Transport in Sewers Using Gene Expression Programming

Document Type : Regular Article

Authors

1 Ph.D. Candidate, Department of Civil Engineering, Razi University, Kermanshah, Iran

2 Professor, Department of Civil Engineering, Razi University, Kermanshah, Iran

Abstract

The deposition of the flow of suspended particles has always been a problematic case in the process of flow transmission through sewers. Deposition of suspended materials decreases transmitting capacity. Therefore, it is necessary to have a method capable of precisely evaluating the flow velocity in order to prevent deposition. In this paper, using Gene-Expression Programming, a model is presented which properly predicts sediment transport in the sewer. In order to present Gene-Expression Programming model, firstly parameters which are effective on velocity are surveyed and considering every of them, six different models are presented. Among the presented models the best is being selected. The results show that using verification criteria, the presented model presents the results as Root Mean Squared Error, RMSE=0.12 and Mean Average Percentage Error, MAPE=2.56 for train and RMSE=0.14 and MAPE=2.82 for verification. Also, the model presented in this study was compared with the other existing sediment transport equations which were obtained using nonlinear regression analysis.

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References

[1]      Butler D, Clark P. Sediment management in urban drainage catchments. Report No. 134, Construction Industry Research and Information Association, London, UK: 1995.
[2]      ASCE. Water pollution control federation: Design and construction of sanitary and storm sewers. American Society of Civil Engineers Manuals and Reports on Engineering Practices, No. 37, Reston, VA: 1970.
[3]      BS8005-1. Sewerage Guide to New Sewerage Construction. British Standard Institution, London, UK: 1987.
[4]      European Standard EN 752-4. Drain and sewer system outside building: Part 4. Hydraulic design and environmental considerations. Brussels: CEN (European Committee for Standardization): Part; 1997.
[5]      Lysne DK. Hydraulic design of self-cleaning sewage tunnels. J Sanit Eng Div 1969;95:17–36.
[6]      Pedroli R. Bed load transportation in channels with fixed and smooth inverts. Scuola Politecnica Federale, Zurigo, Switzerland, 1963.
[7]      GRAF WH, ACAROGLU ER. Sediment transport in conveyance systems (Part 1)/A physical model for sediment transport in conveyance systems. Hydrol Sci J 1968;13:20–39.
[8]      Novak P, Nalluri C. Sediment transport in smooth fixed bed channels. J Hydraul Div 1975;101:1139–54.
[9]      Nalluri C. Sediment transport in rigid boundary channels. Proceeding Euromech 192 Transp. Suspended Solids Open channels, vol. 192, Neubiberg, Germany: 1985, p. 101–4.
[10]    Mayerle R. Sediment transport in rigid boundary channels. University of Newcastle Upon Tyne, UK, 1988.
[11]    Mayerle R, Nalluri C, Novak P. Sediment transport in rigid bed conveyances. J Hydraul Res 1991;29:475–95. doi:10.1080/00221689109498969.
[12]    Ghani A. Sediment transport in sewers. University of Newcastle Upon Tyne, UK, 1993.
[13]    Azamathulla HM, Ab. Ghani A, Fei SY. ANFIS-based approach for predicting sediment transport in clean sewer. Appl Soft Comput 2012;12:1227–30. doi:10.1016/j.asoc.2011.12.003.
[14]    Ota JJ, Nalluri C. Graded sediment transport at limit deposition in clean pipe channel. 28th Congr. Int. Assoc. Hydro-Environmental Eng. Res. Graz, Austria, 1999.
[15]    Vongvisessomjai N, Tingsanchali T, Babel MS. Non-deposition design criteria for sewers with part-full flow. Urban Water J 2010;7:61–77. doi:10.1080/15730620903242824.
[16]    May RWP. Sediment transport in sewers. Hydraulic Research Station, Wallingford, England, Report IT 222; 1982.
[17]    May RWP, Ackers JC, Butler D, John S. Development of design methodology for self-cleansing sewers. Water Sci Technol 1996;33:195–205.
[18]    Ackers JC, Butler D, May RWP. Design of sewers to control sediment problems. Report No. CIRIA 141, Construction Industry Research and Information Association, London, UK: Construction Industry Research and Information Association London; 1996.
[19]    Ackers P, White WR. Sediment transport: new approach and analysis. J Hydraul Div 1973;99:2041–60.
[20]    Ackers P. Sediment aspects of drainage and outfall design. Proc. Int. Symp. Environ. Hydraul. Hong kong, 1991.
[21]    Butler D, May R, Ackers J. Self-Cleansing Sewer Design Based on Sediment Transport Principles. J Hydraul Eng 2003;129:276–82. doi:10.1061/(ASCE)0733-9429(2003)129:4(276).
[22]    Banasiak R. Hydraulic performance of sewer pipes with deposited sediments. Water Sci Technol 2008;57:1743. doi:10.2166/wst.2008.287.
[23]    Ota JJ, Perrusquía GS. Particle velocity and sediment transport at the limit of deposition in sewers. Water Sci Technol 2013;67:959. doi:10.2166/wst.2013.646.
[24]    Safari M-J-S, Aksoy H, Unal NE, Mohammadi M. Non-deposition self-cleansing design criteria for drainage systems. J Hydro-Environment Res 2017;14:76–84. doi:10.1016/j.jher.2016.11.002.
[25]    Mondal SK, Jana S, Majumder M, Roy D. A comparative study for prediction of direct runoff for a river basin using geomorphological approach and artificial neural networks. Appl Water Sci 2012;2:1–13. doi:10.1007/s13201-011-0020-3.
[26]    GAD MI, Khalaf S. Application of sharing genetic algorithm for optimization of groundwater management problems in Wadi El-Farigh, Egypt. Appl Water Sci 2013;3:701–16. doi:10.1007/s13201-013-0114-1.
[27]    Al-Abadi AM. Modeling of stage–discharge relationship for Gharraf River, southern Iraq using backpropagation artificial neural networks, M5 decision trees, and Takagi–Sugeno inference system technique: a comparative study. Appl Water Sci 2016;6:407–20. doi:10.1007/s13201-014-0258-7.
[28]    Ahmadianfar I, Adib A, Taghian M. Optimization of multi-reservoir operation with a new hedging rule: application of fuzzy set theory and NSGA-II. Appl Water Sci 2017;7:3075–86. doi:10.1007/s13201-016-0434-z.
[29]    Azimi H, Bonakdari H, Ebtehaj I, Ashraf Talesh SH, Michelson DG, Jamali A. Evolutionary Pareto optimization of an ANFIS network for modeling scour at pile groups in clear water condition. Fuzzy Sets Syst 2017;319:50–69. doi:10.1016/j.fss.2016.10.010.
[30]    Ebtehaj I, Bonakdari H. Evaluation of Sediment Transport in Sewer using Artificial Neural Network. Eng Appl Comput Fluid Mech 2013;7:382–92. doi:10.1080/19942060.2013.11015479.
[31]    Ebtehaj I, Bonakdari H. Performance Evaluation of Adaptive Neural Fuzzy Inference System for Sediment Transport in Sewers. Water Resour Manag 2014;28:4765–79. doi:10.1007/s11269-014-0774-0.
[32]    Rezaei H, Rahmati M, Modarress H. Application of ANFIS and MLR models for prediction of methane adsorption on X and Y faujasite zeolites: effect of cations substitution. Neural Comput Appl 2017;28:301–12. doi:10.1007/s00521-015-2057-y.
[33]    Singh R, Vishal V, Singh TN. Soft computing method for assessment of compressional wave velocity. Sci Iran 2012;19:1018–24. doi:10.1016/j.scient.2012.06.010.
[34]    Singh R, Vishal V, Singh TN, Ranjith PG. A comparative study of generalized regression neural network approach and adaptive neuro-fuzzy inference systems for prediction of unconfined compressive strength of rocks. Neural Comput Appl 2013;23:499–506. doi:10.1007/s00521-012-0944-z.
[35]    Isanta Navarro R. Study of a neural network-based system for stability augmentation of an airplane. Univ Politècnica Catalunya 2013.
[36]    Gan Z, Yang Z, Li G, Jiang M. Automatic Modeling of Complex Functions with Clonal Selection-Based Gene Expression Programming. Third Int. Conf. Nat. Comput. (ICNC 2007), IEEE; 2007, p. 228–32. doi:10.1109/ICNC.2007.278.
[37]    Azamathulla HM, Ghani AA. Genetic Programming to Predict River Pipeline Scour. J Pipeline Syst Eng Pract 2010;1:127–32. doi:10.1061/(ASCE)PS.1949-1204.0000060.
[38]    Khan M, Azamathulla HM, Tufail M, Ab Ghani A. Bridge pier scour prediction by gene expression programming. Proc Inst Civ Eng - Water Manag 2012;165:481–93. doi:10.1680/wama.11.00008.
[39]    CHANG CK, AZAMATHULLA HM, ZAKARIA NA, GHANI AA. Appraisal of soft computing techniques in prediction of total bed material load in tropical rivers. J Earth Syst Sci 2012;121:125–33. doi:10.1007/s12040-012-0138-1.
[40]    Azamathulla HM, Ahmad Z. Gene-expression programming for transverse mixing coefficient. J Hydrol 2012;434–435:142–8. doi:10.1016/j.jhydrol.2012.02.018.
[41]    Ab. Ghani A, Md. Azamathulla H. Gene-Expression Programming for Sediment Transport in Sewer Pipe Systems. J Pipeline Syst Eng Pract 2011;2:102–6. doi:10.1061/(ASCE)PS.1949-1204.0000076.
[42]    Macke E. About sedimentation at low concentrations in partly filled pipes. (In Ger Mitteilungen, LeichtweissÐInstitut FuÈ r Wasserbau Der Tech Univ t Braunschweig 1982.
[43]    May RWP, Brown PM, Hare GR, Jones KD. Self-cleaning conditions for sewers carryign sediment. Hydraulic Research Ltd (Wallingford), Report SR 221; 1989.
[44]    May RWP. Sediment transport in pipes, sewers and deposited beds. Hydraulic Research Ltd., Wallingford, England, Report SR 320; 1993.
[45]    Nalluri C, Ab-Ghani A. Bed load transport with deposition in channels of circular cross section. Proc. Sixth Int. Conf. Urban Storm Drain., Niagara Falls, Canada: 1993.
[46]    Nalluri C, Ghani AA, El-Zaemey AKS. Sediment transport over deposited beds in sewers. Water Sci Technol 1994;29:125–33.
[47]    Ebtehaj I, Bonakdari H, Sharifi A. Design criteria for sediment transport in sewers based on self-cleansing concept. J Zhejiang Univ Sci A 2014;15:914–24. doi:10.1631/jzus.A1300135.
[48]    John R. Koza. Genetic programming: on the programming of computers by means of natural selection. MIT Press Cambridge, MA, USA; 1992.
[49]    Ferreira C. Algorithm for solving gene expression programming: a new adaptive problems. Complex Syst 2001;13:87–129.
[50]    Ferreira C. Gene expression programming: mathematical modeling by an artificial intelligence. vol. 21. 2nd ed. Springer; 2006.
[51]    Legates DR, McCabe GJ. Evaluating the use of “goodness-of-fit” Measures in hydrologic and hydroclimatic model validation. Water Resour Res 1999;35:233–41. doi:10.1029/1998WR900018.
[52]    Hsu K, Gupta HV, Sorooshian S. Artificial Neural Network Modeling of the Rainfall-Runoff Process. Water Resour Res 1995;31:2517–30. doi:10.1029/95WR01955.
[53]    Jain A, Kumar Varshney A, Chandra Joshi U. Short-Term Water Demand Forecast Modelling at IIT Kanpur Using Artificial Neural Networks. Water Resour Manag 2001;15:299–321. doi:10.1023/A:1014415503476.
[54]    Jain A, Ormsbee LE. Short-term water demand forecast modeling techniques-CONVENTIONAL METHODS VERSUS AI. J Am Water Works Assoc 2002;94:64–72. doi:10.1002/j.1551-8833.2002.tb09507.x.
[55]    Rajurkar MP, Kothyari UC, Chaube UC. Modeling of the daily rainfall-runoff relationship with artificial neural network. J Hydrol 2004;285:96–113. doi:10.1016/j.jhydrol.2003.08.011.
[56]    Maghrebi MF, Givehchi M. Using non-dimensional velocity curves for estimation of longitudinal dispersion coefficient. Proc. seventh Int. Symp. river Eng., 2007, p. 16–8.

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  • Receive Date: 26 May 2017
  • Revise Date: 13 June 2017
  • Accept Date: 14 June 2017

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