A hybrid gene expression programming model for discharge prediction

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@Article{Li:2022:jwama,

• author = "Shicheng Li and James Yang",
• title = "A hybrid gene expression programming model for discharge prediction",
• journal = "Proceedings of the Institution of Civil Engineers - Water Management",
• year = "2022",
• volume = "176",
• number = "5",
• pages = "223--234",
• keywords = "genetic algorithms, genetic programming, gene expression programming, data-driven modelling, flow measurement, grey relational analysis, hydraulics, hydrodynamics, mathematical modelling, simulated annealing, waterways, canals",
• ISSN = "1741-7589",
• URL = "https://www.sciencedirect.com/science/article/pii/S1741758922000026",
• DOI = "doi:10.1680/jwama.21.00037",
• abstract = "The head-discharge relationship of an overflow weir is a prerequisite for flow measurement. Conventionally, it is determined by regression methods. With machine learning techniques, data-driven modelling becomes an alternative. However, a standalone model may be inadequate to generate satisfactory results, particularly for a complex system. With the intention of improving the performance of standard gene expression programming (GEP), a hybrid evolutionary scheme is proposed, which is coupled with grey system theory and probabilistic technique. As a gene filter, grey relational analysis (GRA) eliminates noise and simulated annealing (SA) reduces overfitting by optimising the gene weights. The proposed GEP-based model was developed and validated using experimental data of a submerged pivot weir. Compared with standalone GEP, the GRA-GEP-SA model was found to generate more accurate results. Its coefficients of determination and correlation were improved by 3.6percent and 1.7percent, respectively. The root mean square error was lowered by 24.8percent, which is significant. The number of datasets with an error of less than 10percent and 20percent was increased by 15percent and 12percent, respectively. The proposed approach outperforms classic genetic programming and shows a comparative error level with the empirical formula. The hybrid procedure also provides a reference for applications in other hydraulic issues",

}

Genetic Programming entries for Shicheng Li James Yang

Citations