Risk Assessment of Urban Infrastructure Vulnerability to Meteorological Disasters: A case study of Dongguan, China

Abstract

Effective forecasting and response to meteorological hazards are crucial for safeguarding life, property, and supporting sustainable socioeconomic development. With the rising frequency and severity of meteorological hazards worldwide, this study proposes an enhanced risk assessment framework for urban infrastructure exposed to extreme weather events, with a focus on cascading impacts to critical services such as electricity, communication, and transportation networks (roads and subways). A disaster-loss model is developed to quantify infrastructure vulnerability at various spatial and temporal scales under heavy rainfall conditions, accounting for secondary effects. The model's performance is validated through empirical analysis of a 15-year rainfall event in Dongguan City, China, occurring from September 7-8, 2023. Results indicate the model's ability to predict real-event outcomes with approximately 70% accuracy. This model offers valuable insights for disaster prevention and mitigation strategies, aiding decision-makers in optimizing emergency resource allocation, enhancing disaster response efficiency, and issuing timely public risk warnings to minimize losses.

Publication DOI: https://doi.org/10.1016/j.ijdrr.2024.104943
Divisions: College of Engineering & Physical Sciences > School of Infrastructure and Sustainable Engineering > Civil Engineering
College of Engineering & Physical Sciences
College of Engineering & Physical Sciences > School of Infrastructure and Sustainable Engineering
Aston University (General)
Additional Information: Copyright © 2024 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Publication ISSN: 2212-4209
Data Access Statement: Data will be made available on request.
Last Modified: 27 Nov 2024 08:21
Date Deposited: 01 Nov 2024 11:10
Full Text Link:
Related URLs: https://www.sci ... 7052?via%3Dihub (Publisher URL)
PURE Output Type: Article
Published Date: 2024-11
Published Online Date: 2024-10-30
Accepted Date: 2024-10-30
Authors: Li, Fan
Li, Yan
Rubinato, Matteo (ORCID Profile 0000-0002-8446-4448)
Zheng, Yu
Zhou, Tao

Download

[img]

Version: Accepted Version

Access Restriction: Restricted to Repository staff only

License: Creative Commons Attribution


[img]

Version: Published Version

License: Creative Commons Attribution

| Preview

Export / Share Citation


Statistics

Additional statistics for this record