The evaluation of the effect of nappe breakers on the discharge capacity of trapezoidal labyrinth weirs by ELM and SVR approaches. (December 2018)

• Record Type: Journal Article
• Authors: Bilhan, Omer; Emiroglu, M. Emin; Miller, Carol J.; Ulas, Mustafa
• Is Part Of: Flow measurement and instrumentation. Volume 64(2018)
• Page Start: 71
• Page End: 82
• Publication Date: 2018-12
• Subjects: Labyrinth weir -- Nappe breaker -- Extreme learning machines (ELM) -- Support Vector Regression (SVR); Fluid dynamic measurements -- Periodicals; Flow meters -- Periodicals; Fluides, Dynamique des -- Mesure -- Périodiques; Débitmètres -- Périodiques; 681.2805
• DOI: 10.1016/j.flowmeasinst.2018.10.009
• Languages: English
• ISSNs: 0955-5986

Abstract

Abstract: The labyrinth weir is one type of overflow design used to direct and transfer water in open channels and to provide both routine flow and flood passage over dam spillways. Labyrinth weirs are primarily used at sites where the available spillway width is limited. Due to the increase in crest length, a labyrinth weir provides an increase in discharge capacity relative to conventional weir structures. It is important that the discharge coefficient be accurately represented to ensure appropriate and economical design. The discharge coefficient of trapezoidal labyrinth weirs (TLW) is estimated by using extreme learning machines (ELM) and support vector regression (SVR) techniques in this study. Additional discharge coefficient prediction models have been developed for applications that include the use of nappe breakers (NB). These are frequently included in the design as a mechanism to reduce the impact of vibrations and oscillations on these weirs. A total of 1128 test runs for discharge coefficient measurements of TLW with/without NB were performed in the present study. The statistical criteria used for the evaluation of the performance of models are Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Root Relative Squared Error (RRSE), Mean Absolute Percentage Error (MAPE) and Determination Coefficient (R 2 ). Results of this investigation suggest that the models using Extreme Learning Machines (ELM) and Support Vector Regression (SVR) methods are successful in modeling the discharge coefficient of TLW with/without NB. The best correspondence between model and observation occurred using the ELM model; this resulted in an RMSE for the TLW with/without NB of 0.0188 and 0.0158, respectively.