Design of a Dense Flood-Region Prediction Network for Analyzing the Flood Over Urban Region
Year: 2025
Type: article
Abstract: Hydrological monitoring in urban regions is crucial to analyze hydrology and manage floods. Nevertheless, monitoring flood overflow in urban areas including the degree of flood overflow is frequently insufficient. Hydrological models' ability to provide early flood warnings is limited by this deficiency, making calibration difficult. This work developed a technique for assessing the urban floods depth using deep learning and image recognition methodologies to overcome this constraint. To recognize submerged things in images like a pedestrian's leg or a car's exhaust pipe, this approach uses the object identification model known as the Flood Region-prediction network (FRNet). The mean average precision for water depth identification in a flood images collection was 99%. According to the research, the reference object types submerged in the flood impacts how well FRNet detects flood depth; utilizing a car as the reference objects produced a high range of accuracy than employing a person. Moreover, the accuracy of recognition was considerably improved by image augmentation using learning technology. The presented method uses data as image or video from traffic cameras that are currently in use to extract on-site, continuous, real-time data of water depth. This technology provides an affordable and instantly deployable solution by removing the requirement to install additional water meters.
Source: Global NEST Journal
DOI: 10.30955/gnj.06702
Language: English
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4409504405