State-of-the-Art Techniques for Real-Time Monitoring of Urban Flooding: A Review

• Published in: Water (Basel) Vol. 16; no. 17; p. 2476
• Main Authors: Song, Jiayi, Shao, Zhiyu, Zhan, Ziyi, Chen, Lei
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2024
• Subjects: Accuracy; Algorithms; bibliometric analysis; Bibliometrics; Big Data; cameras; Citation analysis; Cocitation; Collaboration; Computer vision; Floods; Machine learning; monitoring; Publication output; Rain; Remote sensing; risk; Sensors; Software; Systematic review; Technological change; Technology; Visualization; water; weather; United Kingdom > UK; Italy; China
• ISSN: 2073-4441; 2073-4441
• DOI: 10.3390/w16172476

In the context of the increasing frequency of urban flooding disasters caused by extreme weather, the accurate and timely identification and monitoring of urban flood risks have become increasingly important. This article begins with a bibliometric analysis of the literature on urban flood monitoring and identification, revealing that since 2017, this area has become a global research hotspot. Subsequently, it presents a systematic review of current mainstream urban flood monitoring technologies, drawing from both traditional and emerging data sources, which are categorized into sensor-based monitoring (including contact and non-contact sensors) and big data-based monitoring (including social media data and surveillance camera data). By analyzing the advantages and disadvantages of each technology and their different research focuses, this paper points out that current research largely emphasizes more “intelligent” monitoring technologies. However, these technologies still have certain limitations, and traditional sensor monitoring techniques retain significant advantages in practical applications. Therefore, future flood risk monitoring should focus on integrating multiple data sources, fully leveraging the strengths of different data sources to achieve real-time and accurate monitoring of urban flooding.