Modeling Siltation of River Channels Using the Physics-Informed Neural Networks Method and Numerical Simulation
Baydaulet U. Omarova P. Merembayev T. Yedilkhan A.
February 2025Engineered Science Publisher
Engineered Science
2025#33
In this study, we developed a Physics-Informed Neural Network (PINN) model integrated with the Navier-Stokes equations to simulate sediment transport and siltation in river channels. The model was designed to predict sediment deposition in complex hydrodynamic environments, using real-world data from the Syrdarya River and Shardara Reservoir and remote sensing data from Sentinel-1 and Sentinel-2 satellites. The PINN demonstrated a 10-15% reduction in computation time compared to traditional numerical methods like Ansys Fluent while maintaining high accuracy in predicting flow velocity, pressure, and sediment accumulation. By integrating physical laws and real-time data, this model has the potential to significantly improve flood management and river channel design, offering a faster, more adaptive approach to environmental hydrodynamic modeling.
Navier-Stokes , Numerical simulation , Physics-Informed neural networks , river pollution , Siltation , Syrdarya river
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Al-Farabi Kazakh National University, Faculty of Information Technology, Department of Computer Science, Almaty, 050040, Kazakhstan
Institute of Information and Computational Technologies, Almaty, 050010, Kazakhstan
Al-Farabi Kazakh National University
Institute of Information and Computational Technologies
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026