This work presents an experiment involving detailed fluvial flood propagation process. Comparing to the existing flood experiments which collect hydrodynamical data just at gauges, flood evolution process in river channel and flood plain is measured and temporal–spatial data are provided. In the experiment, three inflow patterns are considered to reflect the different severity of the floods. The flood propagation and inundation are captured by using an array of surveillance cameras. The images are pre-processed by applying camera calibration method to correct the barrel distortion. A deep learning technique is introduced to automatically identify inundated area. The inundation process is therefore obtained by identifying image series. In addition to the spatial data, the water level evolutions at three gauges are also monitored to supply detailed hydrodynamic information at gauges. The repeatability of the experiments and reliability of the deep learning technique are verified. The experimental data including spatial and point hydrodynamic features for flood events can be used to systematically validate numerical model and calibrate parameters.

中文翻译：

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一种基于深度学习技术的洪水传播实验

这项工作提出了一个涉及详细的河流洪水传播过程的实验。与现有的仅在仪表上收集水动力数据的洪水实验相比，测量了河道和洪泛区的洪水演变过程，并提供了时空数据。在实验中，考虑了三种流入模式来反映洪水的不同严重程度。洪水传播和淹没是通过使用一系列监控摄像机来捕捉的。通过应用相机校准方法对图像进行预处理以校正桶形失真。引入了深度学习技术来自动识别淹没区域。因此，通过识别图像系列来获得淹没过程。除了空间数据，还监测三个水位计的水位变化，以提供水位计的详细水动力信息。验证了实验的可重复性和深度学习技术的可靠性。包括洪水事件的空间和点水动力特征在内的实验数据可用于系统地验证数值模型和校准参数。