Water Level Detection from CCTV Cameras using a Deep Learning Approach

Abstract

Natural disasters are a global problem that causes widespread losses and damage. A system to provide timely information is required in order to help reduce losses. Flooding is one of the major natural disasters that requires a monitoring and detection system. The traditional flood detection systems use remote sensors such as river water levels and rainfall to provide information to both disaster management professionals and the general public. There is an attempt to use visual information such as CCTV cameras to detect extreme flooding events; however, it requires human experts and consistent attention to monitor any changes. In this paper, we introduce an approach to the automatic river water level detection using deep learning to determine the water level from surveillance cameras. The model achieves 93% accuracy using a single camera location and 83% accuracy using multiple camera locations.