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12:44
Watershed Planning within a Quantitative Scenario Analysis Framework
Published on: July 24, 2016
8.7K
Predicting the impact of urban flooding using open data.
Nataliya Tkachenko1, Rob Procter2, Stephen Jarvis2
1Warwick Institute for the Science of Cities , University of Warwick , Coventry CV4 7AL , UK.
Royal Society Open Science
|June 14, 2016
Summary
Localities actively seeking flood risk information online experienced less severe impacts from flood events. Web search patterns correlate with flood impacts, suggesting informed communities exhibit greater resilience.
Area of Science:
- Environmental Science
- Information Science
- Disaster Management
Background:
- Flood events pose significant risks to communities worldwide.
- Public awareness and preparedness are crucial for mitigating flood impacts.
- Understanding public information-seeking behavior can inform disaster response strategies.
Purpose of the Study:
- To investigate the relationship between public web search patterns for flood risk information and the actual impact of flood events on localities.
- To test the hypothesis that higher public awareness correlates with reduced negative flood impacts.
Main Methods:
- Utilized open data to empirically analyze the connection between web search queries related to flood risk and historical flood impact data.
- Employed eigenvector analysis to identify patterns in aggregated web search data.
- Correlated web search eigenvectors with flood warning and incident reporting datasets.
Main Results:
- Aggregated web search data reflects average rainfall patterns.
- Web search eigenvectors predominantly align with locations experiencing similar flood impacts (2014-2015).
- Significant correlations were found between web search eigenvectors and flood warning/incident data.
Conclusions:
- Public engagement with online flood risk information is linked to community resilience and reduced flood damage.
- Web search behavior can serve as an indicator of community preparedness and potential for resilient actions.
- The findings support the use of digital data to understand and predict community responses to natural disasters.
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