Design of a Dense Flood-Region Prediction Network for Analyzing the Flood Over Urban Region

<p>Hydrological monitoring in urban regions is crucial to analyze hydrology and manage floods.<br>Nevertheless, monitoring flood overflow in urban areas including the degree of flood overflow is<br>frequently insufficient. Hydrological models' ability to provide early flood warnings is limited by<br>this deficiency, making calibration difficult. This work developed a technique for assessing the<br>urban floods depth using deep learning and image recognition methodologies to overcome this<br>constraint. To recognize submerged things in images like a pedestrian's leg or a car's exhaust pipe,<br>this approach uses the object identification model known as the Flood Region-prediction network<br>(FRNet). The mean average precision for water depth identification in a flood images collection<br>was 99%. According to the research, the reference object types submerged in the flood impacts how<br>well FRNet detects flood depth; utilizing a car as the reference objects produced a high range of<br>accuracy than employing a person. Moreover, the accuracy of recognition was considerably<br>improved by image augmentation using learning technology. The presented method uses data as<br>image or video from traffic cameras that are currently in use to extract on-site, continuous, real-time<br>data of water depth. This technology provides an affordable and instantly deployable solution by<br>removing the requirement to install additional water meters.</p>