Neural network solution of inverse parameters used in the sensitivity-calibration analyses of the SWMM model simulations

Advances in Engineering Software

Volumn 32, Issue 7, 2001, Pages 587-595

  Zaghloul, N A ^a   Abu Kiefa, M A ^b  

^a KUWAIT UNIVERSITY   (Kuwait)

^b CAIRO UNIVERSITY   (Egypt)

Author keywords

Artificial neural networks; General regression neural network; PCSWMM98; Sensitivity calibration analyses; Storm water management model; Storm water management model (SWMM)

Indexed keywords

COMPUTER SIMULATION; ERROR ANALYSIS; INVERSE PROBLEMS; MATHEMATICAL MODELS; NEURAL NETWORKS; REGRESSION ANALYSIS; RUNOFF; SENSITIVITY ANALYSIS; STORMS; TRANSPORT PROPERTIES;

RUNOFF-EXTENDED TRANSPORT SIMULATIONS; SENSITIVITY-CALIBRATION ANALYSIS; STORM WATER MANAGEMENT MODELS;

SOFTWARE ENGINEERING;

EID: 0035400575     PISSN: 09659978     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0965-9978(00)00072-7     Document Type: Article

References (18)

1. Abdalla K.M., Stavroulakis G.E. A backpropagation neural network model for semi-rigid steel connections. Microcomputers Civil Engng. 10:1995;77-87.
2. Abu Kiefa M.A. General regression neural networks for driven piles in cohesionless soils. J Geotech Geoenviron Engng, ASCE. 124:(12):1998;1177-1185.
3. Eberhart R.C., Dobbins R.W. Neural network PC tools: a practical guide. 1990;Academic Press, San Diego.
4. Flood I., Kartam N. Neural networks in civil engineering I: principles and understanding. J Computing Civil Engng, ASCE. 8:(2):1994;131-148.
5. Gribb MM, Gribb GW. Use of neural networks for hydraulic conductivity determination in unsaturated soil. Second International Conference on Ground Water Ecology, 1994. p. 155-63.
6. Hammerstrom D. Working with neural networks. IEEE Spectrum. 1993;46-53.
7. Haykin S. Neural networks: a comprehensive foundation. 1994;Macmillan College Publishing Company, New York.
8. James W, Huber WC, Dickinson RE, James WRC, editors. Water Systems Models HYDROLOGY. Users guide to SWMM4 RUNOFF and supporting modules (RAIN, TEMPERATURE, COMBINE and STATISTICS), 1998. 313 pp.
9. James W, Huber WC, Dickinson RE, James WRC, editors. Water Systems Models [2] HYDRAULICS. Users guide to SWMM4 TRANSPORT, EXTRAN and STORAGE modules, 1998. 306 pp.
10. Lin C.T., Lee C.S.G. Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems. 1996;Prentice Hall, Upper Saddle River, NJ.
11. Lipmann R.P. An introduction to computing with neural nets. Acoust Speech Signal Processing. 4:(2):1987;4-22.
12. Mahmoud M.A., Abu Kiefa M.A. Neural Network solution of the inverse vibration problem. NDT&E Int. 32:1999;91-99.
13. Marquez L, Hill T. Function approximation using backpropagation and general regression neural networks. Proc 26th Hawaii Int Conf Sys Sci, 1993. p. 607-15.
14. Najjar Y., Basheer I.A. Utilizing computational neural networks for evaluating the permeability of compacted clay liners. Geotech Geol Engng. 14:1996;193-212.
15. Ohnishi Y, Soliman M. Seepage under Concrete dam Founded on Rock Formation using Artificial Neural Networks. Rock Foundation, Yoshinaka, Kikuchi, editors, 1995. p. 355-60.
16. Rumelhart D.E., McClelland J.L. Parallel Distributed Processing-explorations in the Microstructure of Cognition. vol. 1/2:1986;MIT Press, Cambridge, MA.
17. Specht D.F., Shapiro P.D. Generalization of probabilistic neural networks compared with backpropagation networks. Proc Int Joint Conf Neural Networks. 1991;889-892.
18. Specht D.F. A general regression neural network. IEEE Trans Neural Networks. 2:(6):1991;568-576.