Regression Analysis, Genetic Programming and ANN to Predict Discharge Coefficient of Compound Broad Crested Weir

Document Type : Research Article

Authors

• A. Masoodi
• P. Parsamehr
• F. Salmasi
• S. Pureskandar

10.22067/jsw.v0i0.15297

Abstract

Compound broad-crested weir, have a small inner rectangular section for measuring low flows and then, they broaden to a wide rectangular section at higher flow depths. This paper presents data that will be of use in the design of hydraulic structures for flow control and measurement. A series of laboratory experiments was performed in order to investigate the effects of length of the lower weir crest and step height of broad-crested weirs of rectangular compound cross section on the values of the discharge coefficient. For this purpose, 15 different broad-crested weir models with rectangular compound cross sections for a wide range of discharges tested. Multiple regression equations based on dimensional analysis theory were developed for computing discharge coefficient. The results of compound broad-crested weirs were compared with Genetic programming (GP) and Artificial neural network (ANN) and it was found that the ANN formulation of the problem of solving for the discharge coefficient is less successful than that by GP. The implementation of GP offers another formulation for discharge coefficient.

Keywords

• Compound broad-crested weir
• Discharge coefficient
• Intelligent Artificial

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