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

Riverine bathymetry is of crucial importance for shipping operations and flood management. However, obtaining direct measurements of depth is not always easy. Conversely, with recent advances in sensor technology, indirect measurements can be obtained and used to estimate high-resolution river bed topography. Physics-based inverse modeling techniques have been used to estimate bathymetry using indirect measurements like flow velocity at the surface. However, these methods are computationally expensive for large-scale problems. Recently, deep learning has opened a new door toward knowledge representation and complex pattern identification in many fields; however, these techniques have not been used for high-dimensional riverine bathymetry problems since they require a large amount of data in the training phase to have a good estimation performance that can be generalized for new river profiles. Also, unless one reduces the dimension of the problem, these methods can have a computationally expensive similar to that of physics-based techniques. Here, we develop a new deep learning framework for riverine problems that can be trained using only a few river profiles and in a computationally efficient way that allows finding solutions on personal computers. The proposed method exploits the spatially local connection between the observations and river bed profile and combines a fully connected Deep Neural Network (DNN) with Principal Component Analysis (PCA) to image river bed topography using depth-averaged flow velocity observations. The new method is presented and applied to three riverine bathymetry identification problems. Results show that the proposed method achieves satisfactory performance in bathymetry estimation, providing a powerful data-driven technique for riverine bathymetry in terms of prediction quality, robustness, and computational cost that requires only a relatively small number of training samples.

Automated 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.