Quantitative assessment of urban flood disaster vulnerability based on text data: case study in Zhengzhou

Planning to evaluate flood disaster vulnerability is a crucial step towards risk mitigation and adaptation. In this study, the vulnerability curve model was established with one highly popular area of research in mind: big data. Web crawler technology was used to extract text information related to...

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Bibliographic Details
Published in:Water science & technology. Water supply 2020-03, Vol.20 (2), p.408-415
Main Authors: Wu, Zening, Shen, Yanxia, Wang, Huiliang, Wu, Meimei
Format: Article
Language:English
Subjects:
Access
Access to information
Adaptation
Big Data
Cities
Dependent variables
Digital media
Disasters
Economic impact
Economics
Flood control
Flood management
Floods
Independent variables
Information processing
Information storage
Internet
Methods
Mitigation
Precipitation
Rain
Rainfall
Rainfall index
Rainfall intensity
Risk reduction
Social media
Social networks
URLs
Vulnerability
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Summary:Planning to evaluate flood disaster vulnerability is a crucial step towards risk mitigation and adaptation. In this study, the vulnerability curve model was established with one highly popular area of research in mind: big data. Web crawler technology was used to extract text information related to floods from Internet and social media platforms. Based on the three indicators of rainfall intensity, duration and coverage area, the heavy rainfall index was calculated, while the comprehensive disaster index was calculated based on the affected population, area and direct economic loss. Taking the heavy rainfall index as an independent variable and comprehensive disaster index as a dependent variable, the vulnerability curve of flood disasters was established, and the performance of this model was validated by comparing it with real-life situations. The results show that the relationship between rainfall and disaster is significant, and there is exponential correlation between the heavy rainfall index and comprehensive disaster index. This model is more than 65% accurate, which demonstrates the discriminative power of the established curve model. The results provide some basis for flood control and management in cities.
ISSN:1606-9749
1607-0798
DOI:10.2166/ws.2019.171