Initial assessment of bridge backwater using an artificial neural network approach

Canadian Journal of Civil Engineering

Volumn 35, Issue 5, 2008, Pages 500-510

  Cobaner, Murat ^a   Seckin, Galip ^a   Kisi, Ozgur ^b  

^a CUKUROVA UNIVERSITY   (Turkey)

^b ERCIYES UNIVERSITY   (Turkey)

Author keywords

Artificial neural network; Bridge backwater; Hydraulic model testing; Open channel flow

Indexed keywords

BRIDGES; ELASTIC MODULI; ENERGY DISSIPATION; FLOOD DAMAGE; FLUID MECHANICS; FOOD PROCESSING; HYDRAULICS; IMAGE CLASSIFICATION; NEURAL NETWORKS; REGRESSION ANALYSIS; SHORE PROTECTION; SURVEYS; WATER;

ARTIFICIAL NEURAL NETWORK (ANN) MODELS; ARTIFICIAL NEURAL NETWORK (ANNS); BRIDGE STRUCTURES; ENERGY LOSSES; FIELD DATA; FLOOD FLOWS; HYDRAULIC ENGINEERING; HYDRAULIC RESEARCH; INITIAL ASSESSMENT; INITIAL ESTIMATES; INPUT DATA; MULTIPLE LINEAR REGRESSION (MLR); MULTIPLE LINEAR REGRESSION (MLR) MODELS; UNITED STATES GEOLOGICAL SURVEY (USGS); WALLINGFORD (CO);

BACKPROPAGATION;

ARTIFICIAL NEURAL NETWORK; ASSESSMENT METHOD; BACKWATER; BRIDGE; COMPARATIVE STUDY; FLOOD DAMAGE; HYDROLOGICAL MODELING; MODEL TEST; OPEN CHANNEL FLOW;

ENGLAND; EURASIA; EUROPE; OXFORDSHIRE; UNITED KINGDOM; WALLINGFORD; WESTERN EUROPE;

EID: 47149115136     PISSN: 03151468     EISSN: None     Source Type: Journal    
DOI: 10.1139/L07-142     Document Type: Article

References (53)

1. ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. 2000a. Artificial neural networks in hydrology. I: preliminary concepts. Journal of Hydrologic Engineering, 5(2): 115-123. doi:10.1061/(ASCE)1084-0699 (2000)5:2(115).
2. ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. 2000b. Artificial neural networks in hydrology. II: hydrologic applications. Journal of Hydrologic Engineering, 5(2): 124-137. doi:10.1061/(ASCE)1084-0699 (2000)5:2(124).
3. Alp, M., and Cigizoglu, H.K. 2007. Suspended sediment load simulation by two artificial neural network methods using hydrometeorological data. Environmental Modelling & Software, 22(1): 2-13. doi:10.1016/j. envsoft.2005.09.009.
4. Biery, P.F., and Delleur, J.W. 1962. Hydraulics of single span arch bridge constrictions. Journal of the Hydraulics Division, 88:75-108.
5. Bradley, J.N. 1978. Hydraulics of bridge waterways: hydraulic design. Series no. 1. 2nd ed. Office of Engineering, U.S. Department of Transportation, Federal Highway Administration, Washington, D.C.
6. Brown, P.M. 1985. Hydraulics of bridge waterways: Interium report. Hydraulic Research Wallingford, Wallingford, UK. Report SR 60.
7. Brown, P.M. 1987. Afflux at arch bridges: Second interium report. Hydraulic Research Wallingford, Wallingford, UK. Report SR 115.
8. Brown, P.M. 1988. Afflux at arch bridges. Hydraulic Research Wallingford, Wallingford, UK. Report SR 182.
9. Brunner, W.G., and Hunt, J.H. 1995. A comparison of the one-dimensional bridge hydraulic routines from HEC-RAS, HEC-, and WSPRO. Project No: RD-41. US Army Corps of Engineers, Hydrologic Engineering Center, Davis, Calif.
10. Budcema, M., and Sacco, P.L. 2000. Feedforward networks in financial predictions: the future that modifies the present. Expert Systems, 17(3): 149-170. doi: 10.1111/1468-0394.00137.
11. Chapre, S.C., and Canale, R.P. 1998. Numerical methods for engineers: with programming and software applications. 3rd ed. McGraw-Hill Science/Engineering/Math, Columbus, Ohio.
12. Cigizoglu, H.K. 2003. Estimation, forecasting and extrapolation of river flows by artificial neural networks. Hydrological Sciences Journal, 48(3): 349-361. doi:10.1623/hysj.48.3.349.45288.
13. Cigizoglu, H.K., and Kişi, Ö. 2005. Flow prediction by three back propagation techniques using k-fold partitioning of neural network training data. Nordic Hydrology, 36(1): 49-64.
14. Cobaner, M., Haktanir, T., and Kisi, O. 2008. Prediction of hydropower energy using ANN for the feasibility of hydropower plant installation to an existing irrigation dam. Water Resources Management, 22(6): 757-774. doi:10.1007/s11269-007-9190-z.
15. Colson, B.E., Ming, C.O., and Arcement, G.J. 1978. Backwater at bridges and densely wooded flood plains, Bogue Chitto near Johnston Station, Missisippi. Hydrological Investigations Atlas 591. Department of the Interior, U.S. Geological Survey, Denver, Colo.
16. Coulibaly, P., Anctil, F., and Bobée, B. 1999. Prévision hydrologique par réseaux de neurones artificiels: état de l'art. Canadian Journal of Civil Engineering, 26(3): 293-304. [In French.] doi:10.1139/cjce-26-3-293.
17. Coulibaly, P., Haché, M., Fortin, V., and Bobée, B. 2005. Improving daily reservoir inflow forecasts with model combination. Journal of Hydrologic Engineering, 10(2): 91-99. doi:10.1061/(ASCE)1084-0699(2005) 10:2(91).
18. Fang, Y.-C., and Wu, B.-W. 2007. Neural network application for thermal image recognition of low-resolution objects. Journal of Optics A: Pure and Applied Optics, 9(2): 134-144. doi:10.1088/1464-4258/9/2/003.
19. Goh, A.T.C. 1995. Back-propagation neural networks for modeling complex systems. Artificial Intelligence in Engineering, 9(3): 143-151. doi:10.1016/0954-1810(94)00011-S.
20. Hagan, M.T., and Menhaj, M.B. 1994. Training feed forward techniques with the Marquardt algorithm. IEEE Transactions on Neural Networks, 5(6): 989-993. doi:10.1109/72.329697.
21. Haktanir, T. 1975. A comparative study of three contemporary methods utilized in the hydraulics of bridge waterways. M.Sc. thesis. Civil Engineering Department, Pennsylvania State University, University Park, Pa.
22. HEC. 1991. Water surface profiles user's manual (HEC-2). Hydrologic Engineering Center (HEC), U.S. Army Corps of Engineers, Davis, Calif.
23. HEC 2002. River analysis system (HEC-RAS) users manual. Version 3.1 Beta. Hydrologic Engineering Center (HEC), U.S. Army Corps of Engineers, Davis, Calif.
24. Hornik, K., Stinchcombe, M., and White, H. 1989. Multilayer feedforward networks are universal approximators. Neural Networks, 2(5): 359-366. doi:10.1016/0893-6080(89)90020-8.
25. Hunt, J., Brunner, G.W., and Larock, B.E. 1999. Flow transitions in bridge backwater analysis. Journal of Hydraulic Engineering, 125(9): 981-983. doi:10.1061/(ASCE)0733-9429(1999)125:9 (981).
26. Jain, S.K., Das, D., and Srivastava, D.K. 1999. Application of ANN for reservoir inflow prediction and operation. Journal of Water Resources Planning and Management, 125(5): 263-271. doi: 10.1061/(ASCE)0733-9496(1999)125: 5(263).
27. Kaatz, K.J., and James, W.P. 1997. Analysis of alternatives for computing backwater at bridges. Journal of Hydraulic Engineering, 123(9): 784-792. doi:10.1061/(ASCE)0733-9429(1997)123:9(784).
28. Kişi, O. 2004a. River flow modelling using artificial neural networks. Journal of Hydrologic Engineering, 9(1): 60-63. doi:10.1061/(ASCE)1084-0699(2004)9:1(60).
29. Kişi, O. 2004b. Multi-layer perceptions with Levenberg-Marquardt training algorithm for suspended sediment concentration prediction and estimation. Hydrological Sciences Journal, 49(6): 1025-1040. doi:10.1623/hysj.49.6.1025.55720.
30. Kisi, O. 2005a. Suspended sediment estimation using neuro-fuzzy and neural network approaches. Hydrological Sciences Journal, 50(4): 683-696. doi:10.1623/hysj.2005.50.4.683.
31. Kisi, O. 2005b. Daily river flow forecasting using artificial neural networks and suto-regressive models. Turkish Journal of Engineering and Environmental Science, 29: 9-20.
32. Kisi, O., and Uncuoglu, E. 2005. Comparison of three back-propagation training algorithms for two case studies. Indian Journal of Engineering and Materials Sciences, 12: 434-442.
33. Lin, G.-F., and Chen, L.-H. 2004. A non-linear rainfall-runoff model using radial basis function network. Journal of Hydrology, 289(1-4): 1-8. doi:10.1016/j.jhydrol.2003.10.015.
34. Maier, H.R., and Dandy, G.C. 2000. Neural networks for the prediction and forecasting of water resources variables: a review of modeling issues and applications. Environmental Modelling & Software, 15(1): 101-124. doi:10.1016/S1364-8152(99)00007-9.
35. Mantz, P. 2004. Afflux at bridges and culverts-afflux advisor-user guide and interim report. Environment Agency, UK. R&D Project: W5-110
36. Masters, T. 1993. Practical neural network, recipes c++. Academic Press Professional, Inc., San Diego, Calif.
37. Papik, K., Molnar, B., Schaefer, R., Dombovari, Z., Tulassay, Z., and Feher, J. 1998. Application of neural networks in medicine - a review. Medical Science Monitor, 4(3): 538-546.
38. Parlak, A., Islamoglu, Y., Yasar, H., and Egrisogut, A. 2006. Application of artificial neural network to predict specific fuel consumption and exhaust temperature for a Diesel, engine. Applied Thermal Engineering, 26(8-9): 824-828. doi:10.1016/j.applthermaleng.2005.10.006.
39. Raman, H., and Sunilkumar, N. 1995. Multivariate modelling of water resources time series using artificial neural networks. Hydrological Sciences Journal, 40(2): 145-164.
40. Rumelhart, D.E., McClelland, J.L., and The PDP Research Group. 1986. Parallel recognition in modern computers. In Proceeding: Explorations in the microstructure of Cognition. Vol. 1. Foundations. MIT Press / Bradford Books, Cambridge, Mass.
41. Saad, M., Bigras, P., Turgeon, A., and Duquette, R. 1996. Fuzzy learning decomposition for the scheduling of hydroelectric power systems. Water Resources Research, 32(1): 179-186. doi:10.1029/95WR02971.
42. Seckin, G. 2004. A simple formula for estimating backwater at bridge constrictions. Canadian Journal of Civil Engineering, 31(4): 561-568. doi:10.1139/104-024.
43. Seckin, G., and Atabay, S. 2005. Experimental backwater analysis around bridge waterways. Canadian Journal of Civil Engineering, 32(6): 1015-1029. doi:10.1139/105-048.
44. Seckin, G., Yurtal, R., and Haktanir, T. 1998. Contraction and expansion losses through bridge constrictions. Journal of Hydraulic Engineering, 124(5): 546-549. doi:10.1061/(ASCE)0733-9429(1998)124:5(546).
45. Sherman, J.O. 1990. User's manual for WSPRO. A computer model for water surface profile computation. U.S. Geological Survey, Reston, Va. Report No. FHWA-IP-89-027.
46. Sir William Hallcrow & Partners and HR Wallingford Ltd. 2002. ISIS Flow user manual. Version 2.1. Sir William Hallcrow & Partners and HR Wallingford Ltd., Wallingford, UK.
47. Smith, J., and Eli, R.N. 1995. Neural-network models of rainfall-runoff process. Journal of Water Resources Planning and Management, 121(6): 499-508. doi:10.1061/(ASCE)0733-9496(1995)121:6(499).
48. Sudheer, K.P., Gosain, A.K., Rangan, D.M., and Saheb, S.M. 2002. Modeling evaporation using an artificial neural network algorithm. Hydrological Processes, 16(16): 3189-3202. doi:10.1002/hyp.1096.
49. Sudheer, K.P., Gosain, A.K., and Ramasastri, K.S. 2003. Estimating actual evapotranspiration from limited climatic data using neural computing technique. Journal of Irrigation and Drainage Engineering, 129(3): 214-218. doi:10.1061/(ASCE)0733-9437(2003)129:3(214).
50. Tayfur, G. 2002. Artificial neural networks for sheet sediment transport. Hydrological Sciences Journal, 47(6): 879-892.
51. Yarnell, D.L. 1934. Bridge piers as channel obstructions. U.S. Department of Agriculture, Washington, D.C. Report 442. p. 25.
52. Yuanyou, X., Yanming, X., and Ruigeng, Z. 1997. An engineering geology evaluation method based on an artificial neural network and its application. Engineering Geology, 47(1-2): 149-156. doi:10.1016/S0013-7952(97)00015-X.
53. Zhang, G., Patuwo, B.E., and Hu, M.Y. 1998. Forecasting with artificial, neural networks: the state of the art. International Journal of Forecasting, 14(1): 35-62. doi:10.1016/S0169-2070(97)00044-7.