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- W3131007112 abstract "The subgrid technique provides an efficient way to model coastal hydrodynamics using a relatively coarse grid, but incorporating small-scale geometrical details into the coarse grid model. A typical subgrid model based on the nonlinear shallow water equations applies a certain subgrid algorithm, which is based on either deterministic representation of the mass balance, or stochastically simplified momentum balance applied in the subgrid scale. In this study, we developed a data-driven subgrid approach which extracts the flow characteristics in the subgrid scale from the data pre-calculated from a full grid model. The machine learning algorithm, Random Forest method, was used in the data training process. Compared to the stochastic-based subgrid model, which has the assumption of the isotropic flow field at the subgrid scale, the data-driven subgrid approach takes into account more appropriate upscaling factors representing anisotropic subgrid effects. Test results showed that the model can provide accurate solutions even with a large subgrid ratio." @default.
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- W3131007112 date "2021-06-01" @default.
- W3131007112 modified "2023-10-01" @default.
- W3131007112 title "A data-driven approach to modeling subgrid-scale shallow marsh hydrodynamics" @default.
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- W3131007112 doi "https://doi.org/10.1016/j.coastaleng.2021.103856" @default.
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