A deep learning technique based flood propagation experiment

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.