Disaster Incident Analysis via Algebra Stories

Disaster management requires detailed data from past disasters for policy planning as well as for the generation of disaster exercises and simulations. Post-analysis of disasters is often distributed in official reports, which provide detailed analysis of the events that have happened. However, some information in such reports is often only given implicitly as part of natural language and thus not accessible to classical natural language processing-based text mining. To address this problem, in this paper, we propose to consider the information extraction tasks related to post-disaster report analysis as algebra stories, that can be treated with computer algebra systems together with natural language processing. We applied our enhanced information extraction approach in preliminary experiments to the report of a bushfire in 2009 in Victoria, Australia and used four different tools for solving fire-specific algebra story problems (ASP). Our evaluation shows that these tools have difficulty handling the occurring ASPs.