4.7 Article

Application of deep learning to large scale riverine flow velocity estimation

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Deep learning technique for fast inference of large-scale riverine bathymetry

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Summary: Riverine bathymetry is important for shipping and flood management, and indirect measurements with sensor technology can be used to estimate river bed topography. Physics-based techniques are computationally expensive, while deep learning offers a data-driven approach with potential for efficient training using limited data. The proposed method combines DNN with PCA to image river bed topography using flow velocity observations, showing satisfactory performance in bathymetry estimation with low computational cost and small number of training samples.

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