Crowd-Assisted Flood Disaster Management

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Natural disasters, including floods, cause significant damage to people’s lives and properties and, in recent years, the frequency, complexity, and severity of these events appear to be increasing. Floods, in particular, cause more devastation, death, and economic impact than any other natural disaster. Disaster reporting has now progressed from official media reporting sources to real-time on-site citizen reporters. Crowd-generated content related to disasters and other events is usually identified as Crowdsourced Data (CSD). This data is often termed geospatial CSD or Volunteered Geographic Information (VGI) when the geospatial properties are provided. With advances in technology, the opportunity for citizens to report incidents as CSD is now freely and widely available. However, the quality of CSD remains problematic as it is captured by people of different backgrounds and abilities on a variety of platforms. In general, CSD is deemed unstructured, and its consistency remains poorly described. The improvement and confirmation of quality are very important for CSD use in critical applications such as flood disaster management. This chapter discusses the background, challenges and opportunities, applications, and quality of CSD along with quality evaluation processes tested on the Ushahidi Crowdmap data of the 2011 Australian floods. CSD location availability analysis, relevancy analysis using the Geographic Information Retrieval (GIR), and credibility analysis using a naïve Bayesian network-based model are also discussed. The results of this study revealed that 59% of the ABC’s 2011 Australian flood Crowdmap reports had location availability when the duplicate data were removed. They also show that GIR techniques and that naïve Bayesian models can be successfully applied to assess the CSD’s relevancy and credibility. The fit-for-purpose analysis of CSD for disaster management can significantly improve CSD's precision, reliability, currency, and ability to supplement authoritative data sources by filling information gaps.