Bridge backwater estimation: A comparison between artificial intelligence models and explicit equations

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{Niazkar:2021:SCI,

• author = "Majid Niazkar and Nasser Talebbeydokhti and Seied Hosein Afzali",
• title = "Bridge backwater estimation: A comparison between artificial intelligence models and explicit equations",
• journal = "Scientia Iranica: Transactions on Civil Engineering (A)",
• year = "2021",
• volume = "28",
• number = "2",
• pages = "573--585",
• month = mar # " and " # apr,
• keywords = "genetic algorithms, genetic programming, Hydraulic structures, bridge backwater estimation, explicit equation, artificial neural network, ANN",
• ISSN = "1026-3098",
• eissn = "2345-3605",
• publisher = "Sharif University of Technology",
• URL = "https://scientiairanica.sharif.edu/article_21738.html",
• eprint = "https://scientiairanica.sharif.edu/article_21738_e2c06ad90c2d537acd956ffe65ff1d46.pdf",
• DOI = "doi:10.24200/sci.2020.51432.2175",
• size = "13 pages",
• abstract = "Estimation of bridge backwater has been one of practical challenges in hydraulic engineering for decades. In this study, Genetic Programming (GP) has been applied for estimating bridge backwater for the first time based on the conducted literature review. Furthermore, two new explicit equations are developed for predicting bridge afflux using Genetic Algorithm (GA) and hybrid MHBMO-GRG algorithm. The performances of these models are compared with Artificial Neural Network (ANN) and several explicit equations available in the literature considering both laboratory and field data. Based on five considered performance evaluation criteria, the two new explicit equations outperform the ones available in the literature. Furthermore, GP and ANN achieve the best results in favor of four out of five considered criteria for train and test data, respectively. To be more specific, ANN improves the MSE and R2 values of the explicit equation developed using GA by 44 percent and 12 percent for the test data while GP enhances the corresponding values by 62 percent and 9 percent for the train data. Finally, the results demonstrate that not only artificial intelligence models considerably improve bridge afflux estimation than the explicit equations but also the suggested equations significantly improve the accuracy of the available explicit ones.",
• notes = "School of Engineering, Department of Civil and Environmental Engineering, Shiraz University, Zand Blvd., Shiraz, Iran",

}

Genetic Programming entries for Majid Niazkar Nasser Talebbeydokhti Seied Hosein Afzali

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