Chapter 17 - Appraisal of multigene genetic programming for estimating optimal properties of lined open channels with circular shapes incorporating constant and variable roughness scenarios

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@InCollection{NIAZKAR:2022:WRMCTa,

• author = "Majid Niazkar",
• title = "Chapter 17 - Appraisal of multigene genetic programming for estimating optimal properties of lined open channels with circular shapes incorporating constant and variable roughness scenarios",
• editor = "Mohammad Zakwan and Abdul Wahid and Majid Niazkar and Uday Chatterjee",
• series = "Current Directions in Water Scarcity Research",
• publisher = "Elsevier",
• volume = "7",
• pages = "285--297",
• year = "2022",
• booktitle = "Water Resource Modeling and Computational Technologies",
• ISSN = "2542-7946",
• DOI = "doi:10.1016/B978-0-323-91910-4.00017-0",
• URL = "https://www.sciencedirect.com/science/article/pii/B9780323919104000170",
• keywords = "genetic algorithms, genetic programming, Open channel hydraulics, Circular channel design, Machine learning methods, Multigene genetic programming, Manning's coefficient",
• abstract = "This study employs multigene genetic programming (MGGP) to develop explicit design equations for estimating optimum values of channel geometries with circular shapes. For this application, the design equations were developed by considering both constant and variable Manning's roughness coefficient. With the aid of contour plots, the relative error values of the new design equations were presented for the entire domain of formulas applicability. Furthermore, the performance of the MGGP-based design equations was compared with that of explicit equation available in the literature, artificial neural network (ANN), and genetic programming (GP). It was found that the proposed equations perform better than existing explicit equations in terms of root mean square errors, mean absolute relative errors, and the determination coefficient, while they yielded close results to those of ANN and GP. Since the developed design equations are explicit incorporating dimensionless channel geometries, they can be implemented in MS Excel or other engineering software in light of the optimum design of circular canals and further relevant applications. In a bid to improve the performance of predicting optimum values of channel properties, examining the combination of MGGP with the generalized reduced gradient is suggested for future studies on the optimum channel design",

}

Genetic Programming entries for Majid Niazkar

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