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Hagen Engelmann, & Frank Fiedrich. (2009). DMT-EOC – A combined system for the decision support and training of EOC members. In S. J. J. Landgren (Ed.), ISCRAM 2009 – 6th International Conference on Information Systems for Crisis Response and Management: Boundary Spanning Initiatives and New Perspectives. Gothenburg: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: The first hours after a disaster are essential to minimizing the loss of life. The chance for survival in the debris of a collapsed building for example decreases considerably after 72 hours. However the available information in the first hours after a disaster is limited, uncertain and dynamically changing. A goal in the development of the Disaster Management Tool (DMT) was to support the management of this situation. Its module DMT-EOC specifically deals with problems of the members in an emergency operation centre (EOC) by providing a training environment for computer based table top exercises and assistance during earthquake disasters. The system is based on a flexible and extendible architecture that integrates different concepts and programming interfaces. It contains a simulation for training exercises and the evaluation of decisions during disaster response. A decision support implemented as a multi-agent system (MAS) combines operation research approaches and rule-base evaluation for advice giving and criticising user decisions. The user interface is based on a workflow model which mixes naturalistic with analytic decision-making. The paper gives an overview of the models behind the system components, describes their implementation, and the testing of the resulting system.
Firoj Alam, Ferda Ofli, & Muhammad Imran. (2019). CrisisDPS: Crisis Data Processing Services. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management. Valencia, Spain: Iscram.
Abstract: Over the last few years, extensive research has been conducted to develop technologies to support humanitarian aid
tasks. However, many technologies are still limited as they require both manual and automatic approaches, and
more importantly, are not ready to be integrated into the disaster response workflows. To tackle this limitation, we
develop automatic data processing services that are freely and publicly available, and made to be simple, efficient,
and accessible to non-experts. Our services take textual messages (e.g., tweets, Facebook posts, SMS) as input to
determine (i) which disaster type the message belongs to, (ii) whether it is informative or not, and (iii) what type of
humanitarian information it conveys. We built our services upon machine learning classifiers that are obtained from
large-scale comparative experiments utilizing both classical and deep learning algorithms. Our services outperform
state-of-the-art publicly available tools in terms of classification accuracy.