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Prediction of Discharge Coefficient for Trapezoidal Labyrinth Side Weir Using a Neuro-Fuzzy Approach

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  • Muhammet Emiroglu

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  • Ozgur Kisi

Abstract

Adaptive neuro-fuzzy inference system (ANFIS) is considered for flow over trapezoidal labyrinth side weirs located on a straight channel as a substantial part of distribution channels in irrigation systems and treatment units. To estimate the outflow over a trapezoidal labyrinth side weir, the discharge coefficient in the side weir equation needs to be determined in according with the effective dimensionless parameters which is Froude number, the sidewall angle, the ratios of weir length to channel width, weir length to total crest length and weir height to flow depth. 670 laboratory test results are used for determining discharge coefficient of trapezoidal labyrinth side weirs. The performance of the ANFIS model is compared with artificial neural networks (ANN), non-linear regression (NLR) and multi-linear regression (MLR) models. The comparing criteria used for the evaluation of the models’ performances are root mean square errors (RMSE), mean absolute errors (MAE) and determination coefficient (R 2 ) statistics. Comparison results indicated that the ANFIS technique could be successfully employed in modeling discharge coefficient. It is found that the ANFIS model with RMSE of 0.090 in test period is superior in estimation of discharge coefficient than the nonlinear and linear regression models with RMSE of 0.124 and 0.279, respectively. Copyright Springer Science+Business Media Dordrecht 2013

Suggested Citation

  • Muhammet Emiroglu & Ozgur Kisi, 2013. "Prediction of Discharge Coefficient for Trapezoidal Labyrinth Side Weir Using a Neuro-Fuzzy Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(5), pages 1473-1488, March.
  • Handle: RePEc:spr:waterr:v:27:y:2013:i:5:p:1473-1488
    DOI: 10.1007/s11269-012-0249-0
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    References listed on IDEAS

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    1. Muhammet Emiroglu & Nihat Kaya, 2011. "Discharge Coefficient for Trapezoidal Labyrinth Side Weir in Subcritical Flow," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(3), pages 1037-1058, February.
    2. Hone-Jay Chu & Liang-Cheng Chang, 2009. "Application of Optimal Control and Fuzzy Theory for Dynamic Groundwater Remediation Design," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(4), pages 647-660, March.
    3. Veysel Güldal & Hakan Tongal, 2010. "Comparison of Recurrent Neural Network, Adaptive Neuro-Fuzzy Inference System and Stochastic Models in Eğirdir Lake Level Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(1), pages 105-128, January.
    4. Rama Mehta & Sharad Jain, 2009. "Optimal Operation of a Multi-Purpose Reservoir Using Neuro-Fuzzy Technique," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(3), pages 509-529, February.
    5. K. Durga Rao & C. Pillai, 2008. "Study of Flow Over Side Weirs Under Supercritical Conditions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 22(1), pages 131-143, January.
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    Cited by:

    1. Onur Genç & Özgür Kişi & Mehmet Ardıçlıoğlu, 2014. "Determination of Mean Velocity and Discharge in Natural Streams Using Neuro-Fuzzy and Neural Network Approaches," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(9), pages 2387-2400, July.
    2. Fatih Üneş & Darko Joksimovic & Ozgur Kisi, 2015. "Plunging Flow Depth Estimation in a Stratified Dam Reservoir Using Neuro-Fuzzy Technique," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(9), pages 3055-3077, July.

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