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Friday, 23 March 2018

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GIAN Course on Disaster Information Management Systems, March 12-16, 2018

Disasters, both natural (earthquakes, hurricanes, tsunamis, floods, tornadoes, and volcano eruptions) and man-made (environmental disasters, terrorist attacks and wars) are severe, large-scale and non-routine events that disrupt the normal functioning of a society and cause widespread and overwhelming losses and impacts. In the last decade, disasters have caused more than $800 billion in losses. The 9.0 magnitude Tohoku earthquake caused 15,889 deaths, 6,152 injuries, and 2,601 people missing. As for the destruction of infrastructure is concerned, 127,290 buildings collapsed, 272,788 buildings collapsed partially, and another 747,989 buildings were partially damaged. In the week right after the earthquake, the associated tsunami further triggered nuclear accidents that caused the evacuation of hundreds of thousands of residents who lived within 20 km radius of the Fukushima Daiichi Nuclear Power Plant.
Disaster management is the process of planning and taking actions to minimize the social and physical impact of disasters and reduce community’s vulnerability to the consequences of disasters. The four important phases of a Disaster Management System are preparation, response, recovery, and mitigation. Effective disaster management has become a critical issue for the entire world, especially for disaster-prone countries such as China, Japan, and the United States. Data-driven disaster management refers to applying advanced data collection and analysis technologies to achieve more effective and responsive disaster management.
In this course, we will start by identifying various data sources that provide useful data for disaster management (governmental organizations, private companies, historical data pertaining to similar disasters, social media information identifying various disaster aspects used in the recovery and mitigation processes, etc.), how to effectively capture the status information and improve situational awareness from diverse information sources, and how to effectively capture user interests and deliver the relevant information to them. We will also review the architectures of a couple of Disaster Management Systems including BCIN (Business Continuity Information Network) implemented at Florida International University, as well as techniques to integrate information from different data sources. Data mining is used liberally in these systems to analyze the current and historical data in search of interesting patterns and trends, which form an indispensable basis for decision making. Finally, we will discuss the challenges involved in Disaster Informatics.
The primary objectives of the course are to:
• Understand the extent of losses caused by both, natural and man-made disasters.
• Detailed discussion of various phases of the Disaster Management process.
• Learn about various data sources providing useful data to deal with a disaster event, and how to meaningfully integrate this data in an automated system to effect quick societal recovery from the event.
• Discuss architectures of and techniques employed in existing Disaster Information Systems by studying a few actual systems.
• Discuss challenges involved in the Disaster Informatics process due to involvement of various kinds of data, people of different technical capabilities, types of infromation deemed useful by the users of these systems, and so on.

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