PREDICTION OF SCOUR DEPTH IN DOWNSTREAM OF SKI-JUMP SPILLWAYS USING SOFT COMPUTING TECHNIQUES

Mohammad K. Ayoubloo, Hazi Md. Azamathulla, Zulfequar Ahmad, Aminuddin Ab. Ghani, Javad Mahjoobi, and Amin Rasekh

References

  1. [1] C.M. Wu, Scour at downstream end of dams in Taiwan, Proc.Int. Symp. on River Mechanics, Bangkok, Thailand, I (A 13),1973, 1–6.
  2. [2] R.B.F. Martins, Scouring of rocky river beds by free jetspillways, International Water Power & Dam Construction,27 (4), 1975, 152–153.96
  3. [3] H.Md. Azmathullah, M.C. Deo, & P.B. Deolalikar, Neuralnetworks for estimation of scour downstream of ski-jumpbucket, Journal of Hydraulic Engineering-ASCE, 131 (10),2005, 898–908.
  4. [4] H.Md. Azamathulla, M.C. Deo, & P.B. Deolalikar, Alternativeneural networks to estimate the scour below spillways, Advancesin Engineering Software, 39 (8), 2008, 689–698.
  5. [5] D.P. Solomatine & K.N. Dulal, Model tree as an alternativeto neural network in rainfall–runoff modelling, HydrologicalSciences Journal, 48 (3), 2003, 399–411.
  6. [6] Incyth-lha, Estudio sobre modelo del aliviadero de la Presa Casade Piedra, Informe Final DOH-044-03-82, Ezeiza, Argentina,1982.
  7. [7] S.K. Shevade, S.S. Keerthi, C. Bhattacharyya, & K.R.K.Murthy, Improvements to the SMO algorithm for SVM regres-sion, IEEE Transactions on Neural Networks, 11 (5), 2000,1188–1193.
  8. [8] A. Etemad-Shahidi & J. Mahjoobi, Comparison between M5model tree and neural networks for prediction of significantwave height in Lake Superior, Ocean Engineering, 36(15–16),2009, 1175–1181.
  9. [9] A.J. Smola & B. Sch¨olkopf, A tutorial on support vectorregression, NeuroCOLT Technical Report TR 1998-030, RoyalHolloway College, London, UK, 1988.
  10. [10] J. Platt, Fast training of support vector machines using se-quential minimal optimization, in B. Sch¨olkopf, C.J.C. Burges,& A.J. Smola (Eds.), Advances in Kernel methods – Supportvector learning (Cambridge, MA: MIT Press, 1999), 185–208.
  11. [11] Bureau of Indian Standards, Criteria of hydraulic design ofbucket type energy dissipators (BIS: 7365-1985, New Delhi,India, 1985).
  12. [12] J.R. Quinlan, Learning with continuous classes, Proceedings ofthe Fifth Australian Joint Conference on Artificial Intelligence,World Scientific, Singapore, 1992, 343–348.
  13. [13] V. Vapnik, Statistical learning theory (New York: Springer,1998).
  14. [14] L. Breiman, J.H. Friedman, R.A. Olshen, & C.J. Stone,Classification and regression trees (Belmont, CA: WadsworthStatistical Press, 1984).
  15. [15] J. Mahjoobi & E.A. Mosabbeb, Prediction of significant waveheight using regressive support vector machines, Ocean Engi-neering, 36(5), 2009, 339–347.

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