Multi-gene genetic programming expressions for simulating solute transport in fractures

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@Article{KHAFAGY:2022:JH,

• author = "Mohamed Khafagy and Wael El-Dakhakhni and Sarah Dickson-Anderson",
• title = "Multi-gene genetic programming expressions for simulating solute transport in fractures",
• journal = "Journal of Hydrology",
• volume = "606",
• pages = "127316",
• year = "2022",
• ISSN = "0022-1694",
• DOI = "doi:10.1016/j.jhydrol.2021.127316",
• URL = "https://www.sciencedirect.com/science/article/pii/S0022169421013664",
• keywords = "genetic algorithms, genetic programming, Closed-form solution, Fractured rock, Matrix diffusion, Multi-Gene genetic programming, Solute transport",
• abstract = "In lieu of process-based models, evolutionary artificial intelligence techniques can yield accurate expressions describing complex phenomena. In the current study, closed-form expressions are developed to predict solute transport in a fracture-matrix system as a function of the parameters that describe relevant physical and chemical processes. The study adopts a multi-gene genetic programming approach to approximate a solution of the classical advection-dispersion equation for reactive transport in single, parallel-plate fractures. The approach is employed to obtain an accurate relationship between the hydraulic, geological, and chemical parameters of the fracture-matrix system as inputs and an ensemble of breakthrough curves as outputs. Solutions generated by the developed model showed good agreement with those of corresponding analytical and numerical models. Computationally, the developed approach is highly efficient, particularly when compared with the analytical solution, which typically requires relatively fine discretization to calculate the long-tailed breakthrough curves. Therefore, future work could extend the developed model to simulate field-scale networks and include additional and more complex transport phenomena. This approach advances solute transport behavior predictions through being simpler and computationally more efficient than currently adopted techniques, which is important as the scale of simulation increases from that of a single fracture to a network",

}

Genetic Programming entries for Mohamed Khafagy Wael El-Dakhakhni Sarah Dickson-Anderson

Citations