Evaluation of sediment transport in sewer using artificial neural network

Engineering Applications of Computational Fluid Mechanics

Volumn 7, Issue 3, 2013, Pages 382-392

  Ebtehaj, Isa ^a   Bonakdari, Hossein ^a  

^a RAZI UNIVERSITY   (Iran)

Author keywords

ANN; Bed load; Sediment; Self cleansing; Sewer

Indexed keywords

EID: 84882314134     PISSN: 19942060     EISSN: 1997003X     Source Type: Journal    
DOI: 10.1080/19942060.2013.11015479     Document Type: Article

References (39)

1. Ab. Ghani A (1993). Sediment Transport in Sewers. Ph.D. Thesis, University of Newcastle Upon Tyne, UK.
2. Ackers JC, Butler D, May RWP (1996). Design of Sewers to Control Sediment Problems. Report No. CIRIA 141, Construction Industry Research and Information Association, London.
3. Ashley RM, Bertrand-Krajewski JL, Hvitved-Jacobsen T, Verbanck M (2004). Solid in sewers-20 years of investigation. Water Science and Technology 52(3):73-84.
4. Azamathulla HMD, Ab. Ghani A, Seow YF (2012). ANFIS-based approach for predicting sediment transport in clean sewer. Applied Soft Computing 12(3):1227-1230.
5. Baghalian S, Bonakdari H, Nazari F, Fazli M (2012). Closed-form solution for flow field in curved channels in comparison with experimental and numerical analyses and artificial neural network. Engineering Applications of Computational Fluid Mechanics 6(4):514-526.
6. Bardossy A, Disse M (1993). Fuzzy rule-based models for infiltration. Water Resources Research 29(2):373-382.
7. Beals R, Jackson T (1998). Neural Computing: An Introduction. Department of Computer Science. University of York, IOP Publishing Ltd.
8. Bonakdari H, Larrarte F (2006). Experimental and numerical investigation on self cleansing and shear in sewers. 2nd International IWA Conference, Sewer Operation and Maintenance, SOM 06, Vienna, Austria, 1926.
9. Bonakdari H, Baghalian S, Nazari F, Fazli M (2011). Numerical analysis and prediction of the velocity field in curved open channel using artificial neural network and genetic algorithm. Engineering Application of Computational Fluid Mechanics 5(3):384-396.
10. Butler B, May R, Ackers J (2003) Self-cleansing sewer design based on sediment transport principles. Journal of Hydraulic Engineering ASCE 129(4):276-282.
11. Chang CL, Liao CS (2012). Parameter sensitivity analysis of artificial neural network for predicting water turbidity. World Academy of Science, Engineering and Technology 70:657-660.
12. Cigizoglu HK (2002). Suspended sediment estimation for rivers using artificial neural networks and sediment rating curves. Turkish Journal of Engineering Environmental Sciences 26(1):27-36.
13. Cigizoglu HK (2002). Suspended sediment estimation and forecasting using artificial neural networks. Turkish Journal Engineering Environmental Sciences 26(1):15-25.
14. Dorofki M, Elshafie AH, Gaafar O, Karim OA, Mastura S (2012). Comparison of artificial neural network transfer functions abilities to simulate extreme runoff data. International Conference on Environment, Energy and Biotechnology, Singapore.
15. Gopakumar R, Mujumdar PP (2007). A fuzzy dynamic wave routing model. Hydrological Processes 21:458-467.
16. Jain SK (2001). Development of integrated sediment rating curves using ANNs. Journal of Hydraulic Engineering ASCE 127(1):30-37.
17. Jalalkamali A, Jalalkamali N (2011). Application of hybrid neural modeling and radial basis function neural network to estimate leakage rate in water distribution network. Journal of World Applied Science 15(3):407-414.
18. Kisi O (2004). Multi-layer perceptrons with Levenberg-Marquardt optimization algorithm for suspended sediment concentration prediction and estimation. Hydrological Science Journal 49(6):1025-1040.
19. Kisi O (2005). Suspended sediment estimation using neuro-fuzzy and neural network approaches? Hydrological Science Journal 50(4):683-696.
20. Kisi O (2008). River flow forecasting and estimation using different artificial neural network techniques. Hydrology Research 39(l):27-40.
21. Lin JY, Cheng CT, Chau KW (2006). Using support vector machines for long-term discharge prediction. Hydrological Sciences Journal 51(4):599-612.
22. May RWP (1982). Sediment Transport in Sewers. Hydraulic Research Station, Wallingford, England, Report IT 222.
23. May RWP, Ackers JC, Butler D, John S (1996). Development of design methodology for self-cleansing sewers. Water Science and Technology 33(9):195-205.
24. May RWP, Brown PM, Hare GR, Jones KD (1989). Self-Cleansing Condition for Sewers Carrying Sediment. Hydraulic Research Ltd (Wallingford), Report SR 221.
25. Mayerle R, Nalluri C, Novak P (1991). Sediment transport in rigid bed conveyances. Journal of Hydraulic Research 29(4):475-496.
26. Memarian H, Balasundram HS (2012). Comparison between multi-layer perceptron and radial basis function networks for sediment load estimation in a tropical watershed. Journal of Water Resource and Protection 4:870-876.
27. Moharrampour M, Kherad M, Abachi N, Zoghi M, Asadi Asad Abad MR (2012). Comparison of artificial neural networks ANN and statistics in daily flow forecasting. Advances in Enviromental Biology 6(2):863-868.
28. Muzzammil M (2008). Application of Neural Networks to scour depth prediction at the bridge abutments. Engineering Application of Computational Fluid Mechanics 2(1):30-40.
29. Nalluri C, Ab. Ghani A, EI. Zaemey AKS (1994). Sediment transport over deposited beds in sewers. Water Science Technology 29(2):125-133.
30. Novak P, Nalluri C (1975). Sediment transport in smooth fixed bed channels. Journal of Hydraulic Division, ASCE 101(9): 1139-1154.
31. Raghuwanshi NS, Singh R, Reddy LS (2006). Runoff and sediment yield modeling using artificial neural networks: Upper Siwane River India. Journal of Hydraulic Engineering ASCE 11(1):71-79.
32. Rezaeian Zadeh M, Amin S, Khalili D, Singh VP (2010). Daily outflow prediction by multi layer perception with logistic sigmoid and tangent sigmoid activation functions. Journal of Water Resources Mangement 24(11):2673-2688.
33. Sen Z, Altunkaynak A (2006). A comparative fuzzy logic approach to runoff coefficient and runoff estimation. Hydrological Processes 20:1993-2009.
34. Takagi T, Sugeno M (1985). Fuzzy identification of systems and its applications to modeling and control. Proceedings of the IEEE Transactions on Systems, Man and Cybernetics 15:116-132.
35. Tayfur G, Guldal V (2006). Artificial neural networks for estimating daily total suspended sediment in natural streams. Nordic Hydrology 37(1):69-79.
36. Vongvisessomjai N, Tingsanchali T, Babel MS (2010). Non-deposition design criteria for sewers with part-full flow. Urban Water Journal 7(1):61-77.
37. Wang WC, Chau KW, Cheng CT, Qiu L (2009). A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. Journal of Hydrology 374(3-4):294-306.
38. Wu CL, Chau KW, Fan C (2010). Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques. Journal of Hydrology 389(1-2):146-167.
39. Yang CT, Reza M, Aalami MT (2009). Evaluation of total load sediment transport using AAN. International Journal of Sediment Research 24(3):274-286.