A deep learning workflow enhanced with optical flow fields for flood risk estimation

Owing to the physical and economic impacts of urban flooding, effective flood risk management is of crucial importance. Thus, it is essential to employ reliable techniques for monitoring water levels in urban creeks and detecting abrupt fluctuations in weather patterns. Ground-based cameras alongside a creek offer a cost-effective solution, since they can be deployed for determining water levels through image-based analysis. Previous research has examined the benefits of image processing and artificial intelligence techniques to achieve this goal. However, the current methods only analyze static image features and ignore the valuable motion information that may exist in adjacent frames that are captured minutes apart. In addressing this limitation, our approach involves computing dense optical flow fields from consecutive images taken by a stationary camera and integrating these representations into a deep-learning workflow. We evaluated the capacity of both our method and alternative approaches to measure not only the absolute water level (i.e., whether the water height is low, medium, high, or flooding) but also the relative water level (i.e., whether the water level is rising or falling). The results showed that optical flow-based representations significantly improved the ability to measure the relative water level, while pairs of successive grayscale images effectively determined the absolute water level.