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Improved side weir discharge coefficient modeling by adaptive neuro-fuzzy methodology

  • Water Engineering
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Abstract

In this article, the accuracy of a soft computing technique is evaluated in terms of discharge coefficient prediction of an improved triangular side weir. The process includes simulating the discharge coefficient with the Adaptive Neuro-Fuzzy Inference System (ANFIS). Matlab software is used for ANFIS modeling. To identify the most appropriate input variables, eight different input combinations with various numbers of inputs are examined. The performance of the proposed system is confirmed by comparing the ANFIS and experimental results for the testing dataset. The performance evaluation demonstrates that the ANFIS model with five inputs (Root Mean Square Error (RMSE) of 0.014) is more accurate than the ANFIS model with one input (RMSE = 0.088). The ANFIS model results are also compared with the results obtained from previous regression and soft computing studies.

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Authors and Affiliations

  1. Dept. of Computer System, Faculty of Computer Science and Information Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Shahaboddin Shamshirband & Amir Hossein Zaji

  2. Dept. of Civil Engineering, Razi University, Kermanshah, Iran

    Hossein Bonakdari

  3. Faculty of Mechanical Engineering, Dept. of Mechatronics and Control, University of Nis, Aleksandra Medvedeva 14, 18000, Nis, Serbia

    Dalibor Petkovic

  4. Institute of Ocean and Earth Sciences, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Shervin Motamedi

Authors
  1. Shahaboddin Shamshirband
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  2. Hossein Bonakdari
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  3. Amir Hossein Zaji
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  4. Dalibor Petkovic
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  5. Shervin Motamedi
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Correspondence to Hossein Bonakdari.

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Shamshirband, S., Bonakdari, H., Zaji, A.H. et al. Improved side weir discharge coefficient modeling by adaptive neuro-fuzzy methodology. KSCE J Civ Eng 20, 2999–3005 (2016). https://doi.org/10.1007/s12205-016-1723-7

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  • DOI: https://doi.org/10.1007/s12205-016-1723-7

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