An Assessment of Using Intelligence Fuzzy Models to Tstimate the Sequent Depth and Roller length of B-type Hydraulic Jump

Document Type : Research Paper


1 MSc. Student, Abboreyhan Campus, University of Tehran, Tehran, Iran.

2 Associate Professor, Agricultural Engineering Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran.


Development of irrigation and drainage networks is known as one of the most effective approaches for the optimal use of limited water resources (Abbasi., 2011). But many of the constructed networks suffer from different problems which raised from different reasons. Rahimi et al. (2011) categorized these reasons as; poor design, improper construction operation, low quality of construction materials, poor operation and maintenance, and geotechnical problems of the subgrade materials. Stilling basins are commonly used structures in order to dissipate the energy in downstream of chutes. In general, a hydraulic jump would be created at the end of the chute. The B-type hydraulic jump takes place on the positively inclined plane of a chute under particular conditions. In this condition, determination of the secondary depth and the length of the roller is essential for protecting the structures. Since there is no  proper analytical method to solve the momentum equation for the mentioned condition, using of different smart techniques such as artificial intelligence was considered recently. Caralo et al. (2011) developed different models for determination of the flow characteristics for B-type hydraulic jump. Dusan etal. 2012 presented a neo- Fuzzy system for predicting the weir coefficient.  Akib etal. (2014) predicted the scouring depth by using of neo- Fuzzy system. Azamathulla et al. (2012) developed a neo-fuzzy system for prediction of the sediment transport. In this research, the application of two intelligence methods including Neuro - Fuzzy Inference System and fuzzy inference system were investigated.


Main Subjects

1-    شکریان، م. و م. شفاعی بجستان. 1393. اثر ارتفاع زبری بستر تندآب بر خصوصیات پرش نوع B. مجله علمی پژوهشی آب و خاک، (2)24: 226-215.
2-    شکریان، م. و م. شفاعی بجستان. 1393. برآورد نسبت عمق های مزدوج پرش هیدرولیکی نوع B بر روی بستر صاف. مجله علمی پژوهشی علوم و مهندسی آبیاری، (3)37: 42-32.
3- Abbasi, N. 2011. The role of anions in dispersion potential of clayey soil. Journal of Agricultural Engineering Research, 12(3), IAERI, 12(3): 15-30.
4- Adam, A., M. Ruff, J. F., AlQaser, G. and S.R. Abt. 1993. Characteristics of B-jump with different toe locations. Journal of Hydraulic Engineering, 119(8): 938-948.
5- Akan, A.O. 2011. Open channel hydraulics. Butterworth-Heinemann.
6- Akib, S., Mohammadhassani, M. and A. Jahangirzadeh. 2014. Application of ANFIS and LR in prediction of scour depth in bridges. Computers & Fluids, 91: 77-86.
7- Azamathulla, H.M., Ghani, A.A. and S. Y. Fei. 2012. ANFIS-based approach for predicting sediment transport in clean sewer. Applied Soft Computing, 12(3): 1227-1230.
8- Bradley, J.N. and A. J. Peterka. 1957. The hydraulic design of stilling basins: hydraulic jumps on a horizontal apron (basin i). Journal of the Hydraulics Division, 83(5): 1-24.
9- Carollo, F.G., Ferro, V. and V. Pampalone. 2011. Sequent depth ratio of a B-jump. Journal of Hydraulic Engineering, 137(6): 651-658.
10- Dursun, O.F., Kaya, N. and M. Firat. 2012. Estimating discharge coefficient of semi-elliptical side weir using ANFIS. Journal of Hydrology, 426: 55-62.
11- Hager, W.H. 1988. B-jump in sloping channel. Journal of Hydraulic Research, 26(5): 539-558.
12- Jacquin, A.P. and A. Y. Shamseldin. 2006. Development of rainfall–runoff models using Takagi–Sugeno fuzzy inference systems. Journal of Hydrology, 329(1): 154-173.
13- Karbasi, M. and H. M. Azamathulla. 2016. GEP to predict characteristics of a hydraulic jump over a rough bed. KSCE Journal of Civil Engineering, 20(7): 3006-3011.
14- Kawagoshi, N. and W. H. Hager. 1990. B-jump in sloping channel, II. Journal of Hydraulic Research, 28(4): 461-480.
15- Kosko, B. 1994. Fuzzy systems as universal approximators. IEEE Transactions on Computers, 43(11):1329-1333.
16- Rahimi, H., Abbasi, N. and H. Shantia. 2011. Application of geomembrane to control piping of sandy soil under concrete canal lining (case study: Moghan irrigation project, Iran).  Journal of Irrigation and Drainage Engineering, 60: 330-337.
17- Shirsath, P.B. and A. K. Singh. 2010. A comparative study of daily pan evaporation estimation using ANN, regression and climate based models. Water Resources Management, 24(8): 1571-1581.
18- Yazdandoost, F.Y., Bateni, S.M. and M. Fazeli. 2007. B-Jump: Roller length, sequent depth, and relative energy loss using Artificial Neural Networks. Journal of Hydraulic Research, 45(4): 529-537.
Volume 40, Issue 4
February 2018
Pages 213-225
  • Receive Date: 03 December 2016
  • Revise Date: 22 March 2017
  • Accept Date: 10 April 2017
  • Publish Date: 21 January 2018