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Effective prediction of scour downstream of ski-jump buckets using artificial neural networks

  • Water Resources and the Regime of Water Bodies
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Abstract

The main objective of this research was to analyze and quantify the uncertainty of artificial neural network in prediction of scour downstream ski-jump buckets. Hence, at first, three artificial neural network models were developed to predict depth, length, and width of scour hole. Then, Monte-Carlo simulation was applied in the estimates of artificial neural network modeling procedure. The uncertainties were quantified by means of two criteria: 95 percent prediction uncertainty and d-factor. The results of the artificial neural network models showed superior performance of it in comparison with some empirical formulas because of higher correlation coefficient (R 2 > 0.95) and lower error (RMSE < 1.63). The obtained result from uncertainty analysis of the models revealed the satisfactory performance of them. In this procedure it was clarified that the artificial neural network model for length prediction was more reliable than the others with d-factor and 95 percent prediction uncertainty equal to 2.53 and 92, respectively.

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References

  1. Abbaspour, K.C., Yang, J., Maximov, I., Siber, R., et al., Modeling hydrology and water quality in the prealpine/alpine Thur watershed using SWAT, Journal of Hydrology, 2007, vol. 333, nos. 2–4, pp. 413–430.

    Article  Google Scholar 

  2. Azmatullah, H.Md., Deo, M.C., and Deolalikar, P.B., Neural networks for estimation of scour downstream of a ski-jump bucket, Journal of Hydraulic Engineering, 2005, vol. 131, no. 10, pp. 898–908.

    Article  Google Scholar 

  3. Azmatullah, H.Md., Deo, M.C., and Deolalikar, P.B., Estimation of scour below spillways using neural networks, Journal of Hydraulic Researches, 2006, vol. 44, no. 1, pp. 61–69.

    Article  Google Scholar 

  4. Azamathulla, H.Md., Deo M.C., and Deolalikar, P.B., Alternative neural networks to estimate the scour below spillways, Journal of Advances in Engineering Software, 2008, vol. 39, no. 8, pp. 689–698.

    Article  Google Scholar 

  5. Azamathulla, H.Md., Ghani, A.A., Zakaria, N.A., Siang, L., and Kiat, C., Prediction of scour around hydraulic structure using soft computing technique, International Conference on Water Resources (ICWR 2009), Kedah, Malaysia, 2009.

    Google Scholar 

  6. Azamathulla, H.MD. and Ghani, A.A., ANFIS-based approach for predicting the scour depth at culvert outlets, Journal of Pipeline Systems Engineering and Practice, 2011, vol. 2, no. 1, pp. 35–40.

    Article  Google Scholar 

  7. Bateni, S.M. and Jeng, D.S., Estimation of pile group scour using adaptive neuro-fuzzy approach, Ocean Engineering, 2006, vol. 34, nos. 8–9, pp. 1344–1354.

    Google Scholar 

  8. Coulibaly, P., Anctil, F., and Bobée, B., Daily reservoir inflow forecasting using artificial neural networks with stopped training approach, Journal of Hydrology, 2000, vol. 230, nos. 3–4, pp. 244–257.

    Article  Google Scholar 

  9. D’Agostino, V. and Ferro, V., Scour on alluvial bed downstream of grade-control structures, Journal of Hydraulic Engineering, 2004, vol. 130, no. 1, pp. 24–36.

    Article  Google Scholar 

  10. Damle, P.M., Venkatraman, C.P., and Desai, S.C., Evaluation of scour below skijump buckets of spillways, CWPRS Golden Jubilee Symposia, Poona, India, 1966, vol. I, pp. 154–163.

    Google Scholar 

  11. Dargahi, B., Scour development downstream of a spillway, Journal of Hydraulic Researches, 2003, vol. 41, no. 4, pp. 417–426.

    Article  Google Scholar 

  12. Dreyfus, G., Martinez, J., Samuelides, M., et al., Reseaux de Neurones: Methodologie et Applications, Editions Eyrolles, Paris, France, 2002.

    Google Scholar 

  13. Finnoff, W., Hergert, F., and Zimmermann, H.G., Improving model selection by nonconvergent methods, Neural Networks, 1993, vol. 6, no. 5, pp. 771–783.

    Article  Google Scholar 

  14. Ghahfarokhi, G.S., Gelder, P.V., and Vrijling, J., Probabilistic description of scour hole downstream of flip bucket spillway of large dams, Conference Technical Proceedings (ANCOLD 2008), Delft University of Technology, 2008.

    Google Scholar 

  15. Ghodsian, M. and Azar, F.A., Effect of sediment gradation on scour below free over fall spillway, Third International Symposium on Environmental Hydraulics, Arizona State University, USA, 2001, pp. 1028–1031.

    Google Scholar 

  16. Ghodsian, M. and Azar, A., Scour hole characteristics below free overfall spillway, International Journal of Sediment Research, 2002, vol. 17, no. 4, pp. 304–313.

    Google Scholar 

  17. Ghodsian, M., Melville, B., and Tajkarimi, D., Local scour due to free over fall jet, Proceeding of the Institution of Civil Engineers, Water Management, 2006, no. 4, pp. 253–260.

    Google Scholar 

  18. Goel, A. and Pal, M., Application of support vector machines in scour prediction on grade-control structures, Engineering Applications of Artificial Intelligence, 2009, vol. 22, no. 2, pp. 216–223.

    Article  Google Scholar 

  19. Govindaraju, R.S., Artificial neural network in hydrology. II: hydrologic application, ASCE task committee application of artificial neural networks in hydrology, Journal of Hydrologic Engineering, 2000, vol. 5, no. 2, pp. 124–137.

    Article  Google Scholar 

  20. Goyal, M.K. and Ojha, C.S.P., Estimation of scour downstream of a ski-jump bucket using support vector and M5 model tree, Water Resources Management, 2011, vol. 25, no. 9, pp. 2177–2195.

    Article  Google Scholar 

  21. Guven, A. and Gunal, M., Prediction of scour downstream of grade-control structures using neural networks, Journal of Hydraulic Engineering, 2008, vol. 134, no. 11, pp. 1656–1660.

    Article  Google Scholar 

  22. Hecht-Nielsen, R., Kolmogorov’s mapping neural network existence theorem, 1st IEEE International Joint Conference on Neural Networks, San Diego, 1987, vol. 3, pp. 11–14.

    Google Scholar 

  23. Hoffmans, G.J.C.M., Jet scour in equilibrium phase, Journal of Hydraulic Engineering, 1998, vol. 124, no. 4, pp. 430–437.

    Article  Google Scholar 

  24. Incyth, L., Estudio sobre modelo del aliviadero de la Presa Casa de Piedra, Informe Final, DOH-044-03-82, Ezeiza, Argentina, 1982.

    Google Scholar 

  25. Karunanidhi, N., Grenney, W.J., Whitley, D., and Bovee, K., Neural networks for river flow prediction, Journal of Computing in Civil Engineering, 1994, vol. 8, no. 2, pp. 201–219.

    Article  Google Scholar 

  26. Liriano, S.L. and Day, R.A., Prediction of scour depth at culvert outlets using neural networks, Journal of Hydroinformatics, 2001, vol. 3, no. 4, pp. 231–238.

    Google Scholar 

  27. Lopardo, R.A., Lopardo, M.C., and Casado, J.M., Local rock scour downstream large dams, International Workshop on Rock Scour due to High Velocity Jets, Lausanne, Switzerland, 2002, pp. 55–58.

    Google Scholar 

  28. Machado, L.I., Formulas para Calcular o Limite da Erosao em Leitos Rochosos ou Granulares, XIII Seminario Nacional de Grandes Barragems, Tema 1, Rio de Janeiro, Brazil, 1980, pp. 35–52.

    Google Scholar 

  29. Martins, R.B.F., Scouring of rocky river beds by free jet spillways, International Water Power and Dam Construction, England, 1975, vol. 27, no. 5, pp. 152–153.

    Google Scholar 

  30. Mason, P.J. and Arumugam, K., Free jet scour below dams and flip buckets, Journal of Hydraulic Engineering, 1985, vol. 111, no. 2, pp. 220–235.

    Article  Google Scholar 

  31. Mason, P.J., Effects of air entrainment on plunge pool scour, Journal of Hydraulic Engineering, 1989, vol. 115, no. 3, pp. 385–399.

    Article  Google Scholar 

  32. Mirskhulava, T.E., Mechanism and computation of local and general scour in non cohesive, cohesive soils and rock beds, International Association for Hydraulic Research, Fort Collins, 1967, vol. 3, no. 12, pp. 169–176.

    Google Scholar 

  33. Noori, R., Hoshyaripour, G.A., Ashrafi, K., and Araabi, B.N., Uncertainty analysis of developed ANN and ANFIS models in prediction of carbon monoxide daily concentration, Atmospheric Environment, 2010a, vol. 44, no. 4, pp. 476–482.

    Article  Google Scholar 

  34. Noori, R., Sabahi, M.S., and Karbassi, A.R., Evaluation of PCA and Gamma test techniques on ANN operation for weekly solid waste predicting, Journal of Environmental Management, 2010b, vol. 91, no. 3, pp. 767–771.

    Article  Google Scholar 

  35. Noori, R., Karbassi, A.R., Mehdizadeh, H., and Sabahi, M.S., A framework development for predicting the longitudinal dispersion coefficient in natural streams using artificial neural network, Environmental Progress and Sustainable Energy, 2011, vol. 30, no. 3, pp. 439–449.

    Article  Google Scholar 

  36. Schoklitsch, A., Kolkbildung unter Ueberfallstrahlen, Wasserwirtschaft, 1932, vol. 25, no. 24, pp. 341–343 (in German).

    Google Scholar 

  37. Sen, P., Spillway scour and design of plunge pool, Journal of Irrigation Power, CBIP, India, 1984, vol. 41, no. 1, pp. 51–66.

    Google Scholar 

  38. Tibshirani, R., A comparison of some error estimates for neural network models, Neural Computation, 1996, vol. 8, no. 1, pp. 152–163.

    Article  Google Scholar 

  39. Veronese, A., Erosion of a Bed downstream from an Outlet, Colorado A and M College, Fort Collins, United States, 1937.

    Google Scholar 

  40. Wang, Q.H., Improvement on BP algorithm in artificial neural network, Journal of Qinghai University, 2004, vol. 22, no. 3, pp. 82–84.

    Google Scholar 

  41. Wittler, R.J., Mefford, B.W., Abt, S.R., Ruff, J.F., and Annandale, G.W., Spillway and dam foundation erosion: predicting progressive erosion extents, The First International Conference on Water Resources Engineering, San Antonio, Texas, 1995.

    Google Scholar 

  42. Wu, C.M., Scour at downstream end of dams in Taiwan, International Symposium on River Mechanics, Bangkok, Thailand, 1973, vol. 1, no. 13, pp. 1–6.

    Google Scholar 

  43. Yildiz, D. and üzücek, E., Prediction of scour depth from free falling flip bucket jets, Journal of Water Power Dam Construction, 1994, vol. 46, no. 11, pp. 50–56.

    Google Scholar 

  44. Zounemat-Kermani, M., Beheshti, A., Ataie-Ashtiani, B., and Sabbagh-Yazdi, S., Estimation of current-induced scour depth around pile groups using neural network and adaptive neuro-fuzzy inference system, Applied Soft Computing, 2009, vol. 9, no. 2, pp. 746–755.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Environmental Engineering, Graduate Faculty of Environment, University of Tehran, Tehran, Iran

    R. Noori

  2. Department of Water Resource Research, Water Research Institute, Energy Ministry, Tehran, Iran

    F. Hooshyaripor

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  1. R. Noori
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  2. F. Hooshyaripor
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Correspondence to R. Noori.

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Noori, R., Hooshyaripor, F. Effective prediction of scour downstream of ski-jump buckets using artificial neural networks. Water Resour 41, 8–18 (2014). https://doi.org/10.1134/S0097807814010096

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  • DOI: https://doi.org/10.1134/S0097807814010096

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