Determination of Labyrinth and Arced Labyrinth Weirs Discharge Coefficients Using Support Vector Regression

Document Type : Research Paper

Authors

Abstract

Labyrinth weirs are widely used for controlling and regulating flow rate in rivers and canals as well as for conveying flow from upstream to downstream of dams. In this study, the discharge coefficient of the different kinds of labyrinth weirs was predicted by using support vector regression (SVR) technique. Totally 527 laboratory test data were used for predicting discharge coefficient of four different types of weirs including: normal and inverted labyrinth weirs in flume and arced labyrinth weirs with and without the nappe breakers in reservoir. The root mean squared error (RMSE), coefficient of determination (E or DC), and squared correlation coefficient (R2) statistics were used to evaluate the models’ performance. The obtained results showed that the support vector regression technique had a high capability of predicting discharge coefficient of labyrinth weirs .Statistical evaluation criteria of the best model for normal labyrinth weirs were R2=0.990, DC=0.988, RMSE=0.0077 in validation stage. The results of sensitivity analysis showed that (Fr, HT/p) and (HT/p, α/θ) were the most influential parameters for the labyrinth and arced labyrinth weirs, respectively.  

Keywords

Main Subjects

References

عباسپور ا و ارونقی ه، 1390. پیش­بینی جریان بر روی سرریز مرکب مثلثی- مستطیلی با استفاده از برنامه­ریزی ژنتیک. دهمین کنفرانس هیدرولیک ایران، 17-19 آبان ماه، دانشگاه گیلان، رشت.
Azamathulla H Md, Haghiabi AH and Parsaie A, 2016. Prediction of side weir discharge coefficient by support vector machine technique. Water Science & Technology: Water Supply doi: 10.2166/ws.2016.014.
Christensen NA, 2012. Flow characteristics of arced labyrinth weirs. M Sc thesis, Utah State University, Logan, UT.
Crookston BM and Tullis B, 2010. Hydraulic performance weirs. Proc Int Junior Researcher and Engineer Workshop on Hydraulic structures (IJREWHS ‘10). Edinburgh, U.K.
Gentilini B, 1940. Weirs within Plan Oblique Crest and Zig-zag. Memories and Studies of the Institute of Hydraulics and Hydraulic Regil the Polytechnic of Milan.
Goel A, 2013. Modeling aeration of sharp crested weirs by using support vector machines. World Academy of Science, Engineering and Technology Vol: 7 (12): Schola r.waset.org /1999.8/ 10002382.
Goel A, 2014. Neural network technique for prediction of discharge coefficient and discharge for a weir, Journal of Indian Water Resources Society 34(2): 2014.
Henderson, FM, 1966. Open Channel Flow. Macmillan, New York, USA.
Hanbay D, Baylar A and Batan M, 2009. Prediction of aeration efficiency on stepped cascades by using least square support vector machines. Expert Systems with Applications 36 (3): 4248–4252.
Hay N and Taylor G, 1970. Performance and design of labyrinth weirs. Journal of Hydraulic Engineering, ASCE 96(11): 2337-2357.
Juma IA, Hussein H and AL-Sarraj M, 2014. Analysis of hydraulic characteristics for hollow semi-circular weirs using artificial neural networks. Flow Measurement and Instrumentation 38: 49–53.
Kocahan H, and Taylor G, 2002. Rehabilitation of Black Rock Dam Seepage & Inadequate Spillway. Hydroplus, Inc., Arlington, Va.
Kumar S, Ahmad Z and Mansoor T, 2011. A new approach to improve the discharging capacity of sharp crested triangular plan form weirs. Flow Measurement and Instrumentation 22:175–180.
Mohamed K and Oliver B, 2005. Development of a discharge equation for side weirs using artificial neural networks. Journal of Hydroinformatics 7(1): 31–39.
Parsaie A and Haghiabi AH, 2014. Assessment of some famous empirical equation and artificial intelligent model (MLP, ANFIS) to predicting the side weir discharge coefficient. Applied Research in Water and Wastewater 2: 75-79.
Parsaie A, Yonesi HA and Najafian SH, 2015. Predictive modeling of discharge in compound open channel by support vector machine technique. Modelling Earth Systems and Environment. 1:1. doi: 10.1007/s40808-015-0002-9.
Roushangar K and Koosheh A, 2015. Evaluation of GA-SVR method for modeling bed load transport in gravel-bed rivers. Journal of Hydrology 527: 1142-1152.
Roushangar K, Akhgar S, Salmasi F and Shiri J, 2014. Modeling energy dissipation over stepped spillways using machine learning approaches. Journal of Hydrology 508: 254–265.
Seamons TR, 2014. Labyrinth Weir: A look into geometric variation and its effect on efficiency and design method predictions. M.Sc. thesis, Utah State University, Logan, UT.
Taylor G, 1968. The performance of labyrinth weirs. Ph.D. thesis, University of Nottingham, Nottingham, England.
Vapnik VN, 1995.The Nature of Statistical Learning Theory. Springer, New York.
Vapnik, V N, 1998. Statistical Learning Theory. Wiley, New York, pages 768.
 
Water and Soil Science
Volume 27, Issue 1
June 2017
Pages 173-186

Files

Share

How to cite

Statistics