Determination Discharge Capacity of Triangular Labyrinth Side Weir Using Multi-Layer Neural Network (ANN-MLP)
Sohrab Karimi1 , Hossein Bonakdari1 * and Azadeh Gholami1
http://dx.doi.org/10.12944/CWE.10.Special-Issue1.16
statistic indexes have been used to assess the accuracy of the results. The results of the examinations indicate that using MLP model along with simultaneous use of dimensionless parameters for the purposes of estimating discharge coefficient: the ratio of water behind the weir to the channel width (h/b), ratio of weir crest length to weir height (L/W), relative Froude number (F=V/√(2Side weirs are used in open channels to control flood and the flow passing through it. Discharge capacity is one of the crucial hydraulic parameters of side weirs. The aim of this study is to determine the effect of the intended dimensionless parameters on predicting the discharge coefficient of triangular labyrinth side weir. MAPE, RMSE, and Rgy)) and vertex angle (Ï´), offered the best results (MAPE= 0.67, R2= 0.99, RMSE = 0.009) in comparison with other models.
Copy the following to cite this article:
Karimi S, Bonakdari H, Gholami A. Determination Discharge Capacity of Triangular Labyrinth Side Weir Using Multi-Layer Neural Network (ANN-MLP). Special Issue of Curr World Environ 2015;10(Special Issue May 2015). DOI:http://dx.doi.org/10.12944/CWE.10.Special-Issue1.16
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Karimi S, Bonakdari H, Gholami A. Determination Discharge Capacity of Triangular Labyrinth Side Weir Using Multi-Layer Neural Network (ANN-MLP). Special Issue of Curr World Environ 2015;10(Special Issue May 2015). Available from: http://www.cwejournal.org
