A Hybrid Theory-Driven and Data-Driven Modeling Method for Solving the Shallow Water Equations
نویسندگان
چکیده
In recent years, mountainous areas in China have faced frequent geological hazards, including landslides, debris flows, and collapses. Effective simulation of these events requires a solver for shallow water equations (SWEs). Traditional numerical methods, such as finite difference volume, face challenges discretizing convection flux terms, while theory-based models need to account various factors shock wave capturing propagation direction, demanding high-level understanding the underlying physics. Previous deep learning (DL)-based SWE solvers primarily focused on constructing direct input–output mappings, leading weak generalization properties when terrain data or stress constitutive relations change. To overcome limitations, this study introduces novel that combines theory data-driven methodologies. The core idea is use artificial neural networks compute reduce modeling complexity. Theory-based used tackle complex friction terms purpose ensuring generalization. Our method surpasses by previous DL-based variations. We validated our solver’s capabilities comparing results with analytical solutions, real-world disaster cases, widely Massflow software-generated simulations. This comprehensive comparison confirms ability accurately simulate hazard scenarios showcases strong varying land surface friction. proposed effectively addresses limitations simplifying complexities theory-driven offering promising approach dynamics simulation.
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ژورنال
عنوان ژورنال: Water
سال: 2023
ISSN: ['2073-4441']
DOI: https://doi.org/10.3390/w15173140