Application of the Nearest Neighbor Algorithm to Predict Cavitation Damage on Spillways

Document Type : Original Article



In this paper, the Nearest Neighbor Algorithm has been applied to predict cavitation damage on dam spillways. In this research, based on flow velocity and cavitation index, five different damage levels from ‘no cavitation damage’ to ‘major cavitation damage’ have been determined. The hydraulic characteristics of flow over the Shahid Abbaspour dam spillway were calculated for different flow rates. Then, the Nearest Neighbor Algorithm has been applied to predict cavitation damage levels and locations on this dam spillway for different flow rates. Comparison of the model results with the observed damages occurred during past floods on this spillway structure, shows that this algorithm predict damage levels and locations appropriately. Finally, the efficiency and precision of the model results have been evaluated by some statistical coefficients. Appropriate values of the correlation coefficient (r=0.896), the Mean Absolute Error (MAE=0.101), the Root Mean Square Error (RMSE=0.108) and Efficiency of model (EF=0.813) confirm that the present model can be suitable and efficient.       


Charles, C. S., and F.  Zhou. 1999. Simulation of free surface flow over spillway.  J. Hydraulic Eng. 125(9), 959–967.
Cheng, X., Y., Chen, and L. Luo. 2006. Numerical simulation of air-water two-phase flow over stepped spillways.  SCI CHINA SER E. 49(6), 674–684.
Dong, Z. Y., and P. L. Su. 2006. Cavitation control by aeration and its compressible characteristics.  J. Hydrodyn. 18(4), 499–504.
Dong, Z., Z., Liu, Y., Wu, and D.Zhang. 2008. An experimental investigation of pressure and cavitation characteristics of high velocity flow over a cylindrical protrusion in the presence and absence of aeration.  J. Hydrodyn. 20(1), 60–66.
Fadaei-Kermani., E, G. A., Barani, and M. Ghaeini-Hessaroeyeh. 2013. Investigation of Cavitation Damage Levels on Spillways.  WASJ. 21(1), 73–78.
Fadaei-Kermani., E., and G. A. Barani. 2014. Numerical simulation of flow over spillway based on the CFD method.  SCI IRAN. 21(1),.
Fadaei-Kermani, E., G. A. Barani, and M. Ghaeini-Hessaroeyeh. 2016., "Numerical Detection of Cavitation Damage on Dam Spillway." Civil Engineering Journal 2 (9), pp 484-490.
Falvey, H. T. 1990. Cavitation in chutes and Spillways. Engineering monograph No. 42,
Felder, S. and H., Chanson. 2014. Effects of step pool porosity upon flow aeration and energy dissipation on pooled stepped spillways. Journal of Hydraulic Engineering, 140(4), p.04014002.
Frizell, K., F., Renna, and J. Matos. 2013. "Cavitation Potential of Flow on Stepped Spillways." J. Hydraulic. Eng., 139 (6), pp. 630-636.
United States Department of the Interior –Bureau of Reclamation, Denver, Colorado.
Ghorbani Dashtaki, S., M. Homaee, M. H. Mahdian and M. Kouchakzadeh. 2009, Site-Dependence Performance of Infiltration Models, Water Resour. Manag, 23, pp. 2777-2790.
Hastie, T., R.Tibshirani, and J. Friedman. 2008. The Elements of Statistical Learning. Springer series, California.
Hay, D. 1988. Model prototype correlation: Hydraulic structures.  J. Hydraulic Eng. 113(8), 899–907.
Khatsuria, R. M. 2005. Hydraulics of Spillways and Energy Dissipators. Marcel Dekker, New York, USA.
Mahab Ghodss Consulting Engineers. (1984). Karun Model Spillway. Hydraulic Department, Tehran.
Momber., M. W. 2000. Short Time Cavitation on Concrete.  Comput. Struct. 24(1), 47–52.
Nie., M. 2001. Cavitation Prevention with Roughened Surface.  J. Hydraulic Eng. 127(10), 47–52.
Valero, D. and García-Bartual, R., 2016. Calibration of an air entrainment model for CFD spillway applications. In Advances in Hydroinformatics (pp. 571-582). Springer Singapore.
Xindung, W. and V. Kumar. 2009. Top Ten Algorithm in Data Mining. Taylor & Francis Group, USA.
Xu, W., S., Luo, Q. Zheng, and J., Luo. 2015. Experimental study on pressure and aeration characteristics in stepped chute flows. Science China Technological Sciences, 58(4), pp.720-726.