Detection of Urban Flood Inundation from Traffic Images Using Deep Learning Methods

Pengcheng Zhong, Yueyi Liu (), Hang Zheng and Jianshi Zhao
Additional contact information
Pengcheng Zhong: Dongguan University of Technology
Yueyi Liu: Dongguan University of Technology
Hang Zheng: Dongguan University of Technology
Jianshi Zhao: Tsinghua University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2024, vol. 38, issue 1, No 15, 287-301

Abstract: Abstract Urban hydrological monitoring is essential for analyzing urban hydrology and controlling storm floods. However, runoff monitoring in urban areas, including flood inundation depth, is often inadequate. This inadequacy hampers the calibration of hydrological models and limits their capacity for early flood warning. To address this limitation, this study established a method for evaluating the depth of urban floods using image recognition and deep learning. This method utilizes the object recognition model YOLOv4 to identify submerged objects in images, such as the legs of pedestrians or the exhaust pipes of vehicles. In a dataset of 1,177 flood images, the mean average precision for water depth recognition reached 89.29%. The study also found that the accuracy of flood depth recognition by YOLOv4 is influenced by the type of reference object submerged by the flood; the use of a vehicle as the reference object yielded higher accuracy than using a person. Furthermore, image augmentation with Mosaic technology effectively enhanced the accuracy of recognition. The developed method extracts on-site, real-time, and continuous water depth data from images or video data provided by existing traffic cameras. This system eliminates the need for installing additional water gauges, offering a cost-effective and immediately deployable solution.

Keywords: Urban flood inundation; Image recognition; Deep learning; Water depth (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s11269-023-03669-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:waterr:v:38:y:2024:i:1:d:10.1007_s11269-023-03669-9

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11269

DOI: 10.1007/s11269-023-03669-9

Access Statistics for this article

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) is currently edited by G. Tsakiris

More articles in Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) from Springer, European Water Resources Association (EWRA)
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().