Prediction of Discharge Capacity of Labyrinth Weir with Gene Expression Programming

EasyChair Preprint 2460

Abstract

 This paper proposes a model based on gene expression programming for predicting the discharge coefficient of triangular labyrinth weirs. The parameters influencing discharge coefficient prediction were first examined and presented as crest height ratio to the head over the crest of the weir (p/y), crest length of water to channel width (L/W), crest length of water to the head over the crest of the weir (L/y), Froude number (F=V/√(gy)) and vertex angle (θ) dimensionless parameters. Different models were then presented using sensitivity analysis in order to examine each of the dimensionless parameters presented in this study. In addition, an equation was presented through the use of nonlinear regression (NLR) for the purpose of comparison with GEP. The results of the studies conducted by using different statistical indexes indicated that GEP is more capable than NLR. This is to the extent that GEP predicts discharge coefficient with an average relative error of approximately 2.5% in such manner that the predicted values have less than 5% relative error in the worst model.

Keyphrases: Soft Computing, discharge coefficient, machine learning, nonlinear regression, sensitivity analysis, weir

@booklet{EasyChair:2460,
  author    = {Hossein Bonakdari and Isa Ebtehaj and Bahram Gharabaghi and Ali Sharifi and Amir Mosavi},
  title     = {Prediction of Discharge Capacity of Labyrinth Weir with Gene Expression Programming},
  howpublished = {EasyChair Preprint 2460},
  year      = {EasyChair, 2020}}