Abstract 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.

中文翻译：

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用于快速推理大规模河流水深测量的深度学习技术

摘要 河流水深测量对于航运作业和洪水管理至关重要。然而，获得深度的直接测量并不总是那么容易。相反，随着传感器技术的最新进展，可以获得间接测量并用于估计高分辨率河床地形。基于物理的逆向建模技术已被用于通过间接测量（如地表流速）来估计测深。然而，这些方法对于大规模问题在计算上是昂贵的。最近，深度学习为许多领域的知识表示和复杂模式识别打开了一扇新的大门；然而，这些技术尚未用于高维河流测深问题，因为它们在训练阶段需要大量数据才能具有良好的估计性能，可以推广到新的河流剖面。此外，除非减少问题的维度，否则这些方法的计算成本可能类似于基于物理的技术。在这里，我们为河流问题开发了一个新的深度学习框架，该框架可以仅使用少数河流剖面进行训练，并以计算效率高的方式允许在个人计算机上找到解决方案。所提出的方法利用观测和河床剖面之间的空间局部联系，并将完全连接的深度神经网络 (DNN) 与主成分分析 (PCA) 相结合，使用深度平均流速观测对河床地形进行成像。提出了新方法并将其应用于三个河流测深识别问题。结果表明，所提出的方法在测深估计中取得了令人满意的性能，在预测质量、鲁棒性和计算成本方面为河流测深提供了一种强大的数据驱动技术，只需要相对较少的训练样本。