A Dual-Layer Complex Network-Based Quantitative Flood Vulnerability Assessment Method of Transportation Systems

Jiayu Ding, Yuewei Wang () and Chaoyue Li
Additional contact information
Jiayu Ding: School of Future Technology, China University of Geosciences, Wuhan 430074, China
Yuewei Wang: Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan 430074, China
Chaoyue Li: School of Future Technology, China University of Geosciences, Wuhan 430074, China

Land, 2024, vol. 13, issue 6, 1-27

Abstract: Evaluating the vulnerability of urban transportation systems to flood disasters can provide scientific support for urban disaster prevention and mitigation. Current methods for assessing the flood vulnerability of urban roads often overlook the internal relationships within the complex spatial composition of road networks and surface structures. In this study, based on the theory of complex networks, a dual-layer network assessment model is established for evaluating the flood vulnerability of urban transportation systems by coupling basic geographic data with road network vector data. Unlike traditional methods, this model considers the complex relationship between road network structures and ground surfaces, uncovering a correlation between road network structure and road flood vulnerability. By utilizing this model, the flood vulnerability of road networks in Shenzhen, as well as the city’s spatial flood vulnerability, are quantitatively assessed. Based on the quantitative results, we create maps illustrating the distribution of road and spatial flood vulnerability in Shenzhen. The study results reflect that roads highly vulnerable to flooding are mainly located in the central urban area of the southwest, with the flood vulnerability spatially concentrated primarily in the northern and western regions. Using data from government reports, news stories, and other sources over the past five years, we compile recorded instances of urban waterlogging. The quantitative results of the model are consistent with the distribution trend in recorded waterlogging points, indicating that the model’s outcomes are authentic and reliable.

Keywords: flood vulnerability; complex network; remote sensing; urban waterlogging; dual-layer network; risk assessment (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (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)
https://www.mdpi.com/2073-445X/13/6/753/pdf (application/pdf)
https://www.mdpi.com/2073-445X/13/6/753/ (text/html)

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:gam:jlands:v:13:y:2024:i:6:p:753-:d:1403616

Access Statistics for this article

Land is currently edited by Ms. Carol Ma

More articles in Land from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().