Application of machine learning methods on investigation the effect of interrupted deflector's height and downstream depth changes on energy dissipation at flip bucket spillways

Document Type : Research Paper


1 Civil Engineering and Architecture Faculty, Shahid Chamran University of Ahvaz, Ahvaz, Iran.

2 Hydraulic Structure Department, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.


Flip bucket is a type of energy dissipator structure. Flip buckets can sometimes be improved by adding wedge-shaped deflectors. This research introduced the best height proportion of used deflector on the flip buckets to increase energy dissipation. It used 4 types of deflector series including 32, 45, 47 and 55 degrees toward horizon with two different regular and irregular layouts and 19 different heights from 2.31 to 5.6 cm and in different hydraulic condition and the results were compared with a flip bucket without a deflector. The characteristics of laboratory flume were: length= 7.5 m, width= 0.58 m and height= 1.6 m. The results illustrates that the energy dissipation in the model with deflectors increased from 11.83 to 19.38 percent as compared with model without a deflector. The greatest percentage of energy dissipation was 80.74% which observed at a deflector angle of 55° and a discharge of 10 L/s, at deflector’s ratio of n=0.8 and in non-uniform layout and in free hydraulic jump. Larger deflector angles and side lengths initially boosted energy dissipation, but this effect plateaued or even reversed at very large angles. For calculating energy dissipation and hydraulic jump length parameters, the regression relations were extracted in this research and results of this relations were compared with results of the gene expression programming (GEP), random forest (RF) and multivariate adaptive regression splines (MARS) methods. The results showed that the RF method is the most accurate method for calculating energy dissipation and hydraulic jump length parameters.


Main Subjects

  1. Arjenaki MO, Sanayei HRZ, (2020). Numerical investigation of energy dissipation rate in stepped spillways with lateral slopes using experimental model development approach. Model Earth Syst Environ 6(2): 605-616.
  2. Hager W.H., Energy Dissipators and Hydraulic Jump. Water Science and Technology Library, vol 8. Springer, Dordrecht, pp.1-4,1992.
  3. Vischer D.L., Hager W.H., Energy dissipators: IAHR hydraulic structures design manual 9. 1st Edition, Routledge, London, 208 p, 1995.
  4. Steiner R., Heller V., Hager W.H., Minor H.E., (2008). Deflector ski jump hydraulics. J Hydraul Eng 134(5): 562–571. (ASCE)0733-9429(2008)134:5(562).
  5. Khatsuria R.M., Hydraulics of spillways and energy dissipators. 1st Edition, CRC Press, Boca Raton, 680 p, 2004.
  6. Khalifehei K., Askari M.S., Azamathulla H., (2022). Experimental investigation of energy dissipation on flip buckets with triangular deflectors. ISH J Hydraul Eng 28(sup1): 292-298.
  7. Novak P., Moffat A.I.B., Nalluri C., Narayanan R., Hydraulic structures. 4th Edition, CRC Press, London, 736 p, 2007.
  8. Chadwick A., Morfett J., Hydraulics in civil and environmental engineering. 1st Edition, CRC Press, London, 632 p, 2002.
  9. Mason P. (1984). Erosion of plunge pools downstream of dams due to the action of free-T-rajectory jets. P I Civil Eng 76(2): 523–537. doi:10.1680/iicep.1984.1257
  10. Heller V., Hager W.H., Minor H.E., (2005). Ski jump hydraulics. J Hydraul Eng 131 (5): 347–355. doi:10.1061/(ASCE)0733-9429(2005)131:5(347)
  11. Yamini O.A., Kavianpour M.R., Mousavi S.H., Movahedi A., Bavandpour M., (2018). Experimental investigation of pressure fluctuation on the bed of compound flip buckets. ISH J Hydraul Eng 24(1): 45–52. doi:10.1080/ 09715010.2017.1344572
  12. Nugroho J., Soekarno I., Soeharno A.W.H., (2019). Experimental study of energy dissipation at baffled chute spillway. Jurnal Teknik Sipil, 26(1): 33-38. DOI: 10.5614/jts.2019.26.1.5
  13. Deng J., Wei W., Tian Z., Zhang F., (2018). Design of a streamwise-lateral ski-jump flow discharge spillway. Water 10(11): 1585.
  14. Lian J., He J., Gou W., Ran D., (2019). Effects of bucket type and angle on downstream nappe wind caused by a turbulent jet. Int J Env Res Pub He 16(8): 1360.
  15. Choi C.E., Ng C.W.W., Goodwin S.R., Liu L.H.D., Cheung W.W., (2016). Flume investigation of the influence of rigid barrier deflector angle on dry granular overflow mechanisms. Can Geotech J 53(10): 1751-1759.
  16. Daneshfaraz R., Ghaderi A., Akhtari A., Francesco S.D., (2020). On the effect of block roughness in ogee spillways with flip buckets. Fluids 5(4): 182.
  17. Pourabdollah N., Heidarpour M., Koupai J.A., (2020). Characteristics of free and submerged hydraulic jumps in different stilling basins. P I Civil Eng-Wat M 173(3): 121–131.
  18. Heidarian P., Neyshabouri S.A.A.S., Khoshkonesh A., Bahmanpouri F., Nsom B., Eidi A., (2022). Numerical study of flow characteristics and energy dissipation over the slotted roller bucket system. Model Earth Syst Environ 8(4):5337-5351.
  19. Sun M., Li Y., (2020). Eco-environment construction of English teaching using artificial intelligence under big data environment. IEEE Access 8: 193955–193965. doi: 10.1109/ACCESS.2020.3033068
  20. Akhbari A., Ibrahim S., Zinatizadeh A.A., Bonakdari H., Ebtehaj I., Khozani Z.S., Vafaeifard M., Gharabaghi B., (2019). Evolutionary prediction of biohydrogen production by dark fermentation. Clean-Soil Air Water 47(1): 1700494.
  21. Karbasi M., Azamathulla H.M., (2016). GEP to predict characteristics of a hydraulic jump over a rough bed. KSCE J Civ Eng 20(7): 3006–3011.
  22. Roushangar K., Ghasempour R., (2018). Explicit prediction of expanding channels hydraulic jump characteristics using gene expression programming approach. Hydrol Res 49(3): 815–830.
  23. Salmasi F., (2021). Effect of downstream apron elevation and downstream submergence in discharge coefficient of ogee weir. ISH J Hydraul Eng 27(4): 375-384.
  24. Nasrabadi, Mehri Y., Ghassemi A., Omid M.H., (2021). Predicting submerged hydraulic jump characteristics using machine learning methods. Water Supply 21(8): 4180–4194.
  25. Bagatur T., Onen F., (2016). Computation of design coefficients in ogee-crested spillway structure using GEP and regression models. KSCE J Civ Eng 20(2): 951–959.
  26. Samadi M., Sarkardeh H., Jabbari E., (2021). Prediction of the dynamic pressure distribution in hydraulic structures using soft computing methods. Soft Comput 25(5): 3873-3888.
  27. ASCE (2000). Hydraulic modeling, Concepts and practice. Task Committee on Hydraulic Modeling, Environmental and Water Resources Institute, ASCE, Edited by Ettema, MOP 97.
  28. Govinda Rao N.S., Rajaratnam N., (1963). The Submerged Hydraulic Jump. J Hydraul Div, 89(HY1): 139-162.
  29. Chow V.T., Open Channel Hydraulics. McGraw-Hill, New York, U.S.A., 680 p, 1959.
  30. Ferreira C. (2001). Gene expression programming: a new adaptive algorithm for solving problems. Complex Systems, 13(2): 87-129.
  31. Ferreira C., Gene expression programming: mathematical modeling by an artificial intelligence. 2nd edition, Springer, Berlin, Germany, 480 p, 2006.
  32. Bagherzadeh M., Mousavi F., Manafpour M., Mirzaee R., Hoseini K., (2022). Numerical simulation and application of soft computing in estimating vertical drop energy dissipation with horizontal serrated edge, Water Supply 22(4): 4676-4689.
  33. Teodorescu L., Sherwood D., (2008). High energy physics event selection with gene expression programming. Comput Phys Commun 178(6): 409–419.
  34. Kamari A., Sattari M., Mohammadi A.H., Ramjugernath D., (2016). Rapid method for the estimation of dew point pressures in gas condensate reservoirs. J Taiwan Inst Chem E 60: 258–266.
  35. Breiman L., (2001). Random Forests. Mach Learn 45(1): 5–32.
  36. Lotfirad M., Esmaeili-Gisavandani H., Adib A., (2022). Drought monitoring and prediction using SPI, SPEI, and random forest model in various climates of Iran. J Water Clim Change 13(2): 383-406.
  37. Friedman J.H., (1991). Multivariate adaptive regression splines. Ann Stat 19(1): 1-67.