Utilising social media data to evaluate urban flood impact in data scarce cities
Bibliographic Data
| ID | 22028435 |
|---|---|
| Authors | Kaihua Guo (0000-0001-9855-3241, University of Hong Kong), Mingfu Guan (0000-0002-5684-4697, University of Hong Kong, corresponding author), Haochen Yan (0000-0002-4198-1162, University of Hong Kong) |
| Year | 2023 |
| Volume | 93 |
| Pages | 103780 |
| Publication date | 2023-07-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Disaster Risk Reduction (JOURNAL) |
| Journal identifiers | ISSN: 2212-4209 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.ijdrr.2023.103780 |
| OpenAlex | W4379209754 |
| Language | EN |
| Citations received | 10 |
| References cited | 27 |
Data mining · Data science · Data source · Database · Environmental planning · Flood myth · Geography · Social media · Workflow · World Wide Web · Computer Science · Environmental Science · Flood Risk Assessment and Management · Public Relations and Crisis Communication · Tropical and Extratropical Cyclones Research
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Multi-crowdsourced data fusion for modeling link-level traffic resilience to adverse weather events
Assessment of urban flood disaster responses and causal analysis at different temporal scales based on social media data and machine learning algorithms
Using social media data to construct and analyze knowledge graph for "7.20" Henan rainstorm flood event
Increasing urban flooding facing metro system
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Unraveling the factors behind self-reported trapped incidents in the extraordinary urban flood disaster
Rapid assessment of disaster damage using social media activity
Processing Social Media Messages in Mass Emergency
Assessing disaster impacts and response using social media data in China
Validating city-scale surface water flood modelling using crowd-sourced data
A novel approach to leveraging social media for rapid flood mapping
Urban resilience from the lens of social media data
Mining Twitter Data for Improved Understanding of Disaster Resilience
| Unique citing works | 10 |
|---|---|
| Citations per year | 5 |
| Citation span | 2024 - 2026 (3) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 10 |