Assessment of artificial intelligence models for calculating optimum properties of lined channels

Abstract

Abstract Lined channels with trapezoidal, rectangular and triangular sections are the most common manmade canals in practice. Since the construction cost plays a key role in water conveyance projects, it has been considered as the prominent factor in optimum channel designs. In this study, artificial neural networks (ANN) and genetic programming (GP) are used to determine optimum channel geometries for trapezoidal-family cross sections. For this purpose, the problem statement is treated as an optimization problem whose objective function and constraint are earthwork and lining costs and Manning's equation, respectively. The comparison remarkably demonstrates that the applied artificial intelligence (AI) models achieved much closer results to the numerical benchmark solutions than the available explicit equations for optimum design of lined channels with trapezoidal, rectangular and triangular sections. Also, investigating the average of absolute relative errors obtained for determination of dimensionless geometries of trapezoidal-family channels using AI models shows that this criterion will not be more than 0.0013 for the worst case, which indicates the high accuracy of AI models in optimum design of trapezoidal channels.