Prediction of scour depth in downstream of ski-jump spillways using soft computing techniques

International Journal of Computers and Applications

Volumn 33, Issue 1, 2011, Pages 92-97

  Ayoubloo, Mohammad K ^a   Azamathulla, Hazi Md ^b   Ahmad, Zulfequar ^c   Ghani, Aminuddin Ab ^b   Mahjoobi, Javad ^a   Rasekh, Amin ^d  

^a IRAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Iran)

^b SCHOOL OF PHYSICS   (Malaysia)

^c INDIAN INSTITUTE OF TECHNOLOGY ROORKEE   (India)

^d TEXAS A AND M UNIVERSITY   (United States)

Author keywords

CART; Model tree; Neural networks; Scour; Soft computing; Spillways; Svm

Indexed keywords

CART; CART MODELS; CLASSIFICATION AND REGRESSION TREE; COMPUTING METHODS; CRITICAL PHENOMENON; MODEL TREE; SCOUR DEPTH; SOFTCOMPUTING TECHNIQUES; SVM; TESTING DATA;

FORECASTING; NEURAL NETWORKS; PLANT EXTRACTS; SOFT COMPUTING; STATISTICAL TESTS;

SPILLWAYS;

EID: 79957841321     PISSN: 1206212X     EISSN: None     Source Type: Journal    
DOI: 10.2316/Journal.202.2011.1.202-3078     Document Type: Article

References (15)

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. R.B.F. Martins, Scouring of rocky river beds by free jet spillways, International Water Power & Dam Construction, 27 (4), 1975, 152-153.
3. H.Md. Azmathullah, M.C. Deo, & P.B. Deolalikar, Neural networks for estimation of scour downstream of ski-jump bucket, Journal of Hydraulic Engineering-ASCE, 131 (10), 2005, 898-908. (Pubitemid 41419825)
4. H.Md. Azamathulla, M.C. Deo, & P.B. Deolalikar, Alternative neural networks to estimate the scour below spillways, Advances in Engineering Software, 39 (8), 2008, 689-698. (Pubitemid 351471778)
5. D.P. Solomatine & K.N. Dulal, Model tree as an alternative to neural network in rainfall-runoff modelling, Hydrological Sciences Journal, 48 (3), 2003, 399-411.
6. Incyth-lha, Estudio sobre modelo del aliviadero de la Presa Casa de Piedra, Informe Final DOH-044-03-82, Ezeiza, Argentina, 1982.
7. S.K. Shevade, S.S. Keerthi, C. Bhattacharyya, & K.R.K. Murthy, Improvements to the SMO algorithm for SVM regression, IEEE Transactions on Neural Networks, 11 (5), 2000, 1188-1193.
8. A. Etemad-Shahidi & J. Mahjoobi, Comparison between M5′ model tree and neural networks for prediction of significant wave height in Lake Superior, Ocean Engineering, 36(15-16), 2009, 1175-1181.
9. A.J. Smola & B. Schölkopf, A tutorial on support vector regression, NeuroCOLT Technical Report TR 1998-030, Royal Holloway College, London, UK, 1988.
10. J. Platt, Fast training of support vector machines using sequential minimal optimization, in B. Schölkopf, C.J.C. Burges, & A.J. Smola (Eds.), Advances in Kernel methods - Support vector learning (Cambridge, MA: MIT Press, 1999), 185-208.
11. Bureau of Indian Standards, Criteria of hydraulic design of bucket type energy dissipators (BIS: 7365-1985, New Delhi, India, 1985).
12. J.R. Quinlan, Learning with continuous classes, Proceedings of the Fifth Australian Joint Conference on Artificial Intelligence, World Scientific, Singapore, 1992, 343-348.
13. V. Vapnik, Statistical learning theory (New York: Springer, 1998).
14. L. Breiman, J.H. Friedman, R.A. Olshen, & C.J. Stone, Classification and regression trees (Belmont, CA: Wadsworth Statistical Press, 1984).
15. J. Mahjoobi & E.A. Mosabbeb, Prediction of significant wave height using regressive support vector machines, Ocean Engineering, 36(5), 2009, 339-347.