Data mining and machine learning in the context of disaster and crisis management Online publication date: Tue, 03-Mar-2015
by Adam T. Zagorecki; David E.A. Johnson; Jozef Ristvej
International Journal of Emergency Management (IJEM), Vol. 9, No. 4, 2013
Abstract: Disaster and crisis situations are characterised by high dynamics and complexity with human lives and substantial environmental and economic consequences at stake. The advances in information technology have had a profound impact on disaster management by making unprecedented volumes of data available to the decision makers. This has resulted in new challenges related to the effective management of large volumes of data. In this paper, we discuss the application of data mining and machine learning techniques to support the decision-making processes for the disaster and crisis management. We discuss the challenges and benefits of the automated data analysis to different phases of crisis management. Based on the literature review, we observe a trend to move from narrow in scope, problem-specific applications of data mining and machine learning to solutions that address a wider spectrum of problems, such as situational awareness and real-time threat assessment using diverse streams of data.
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