Title: Data mining and machine learning in the context of disaster and crisis management

Authors: Adam T. Zagorecki; David E.A. Johnson; Jozef Ristvej

Addresses: Centre for Simulation and Analytics, Cranfield University, Defence Academy of the UK, Shrivenham SN6, 8LA, UK ' Department of Political Science, Missouri State University, 901 S National Ave, Springfield, MO 65804, USA ' Department of Crisis Management, University of Zilina, Univerzitna 1, 010 26 Zilina, Slovakia

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.

Keywords: disaster management; emergency management; crisis management; data analysis; data mining; machine learning; decision making; situational awareness; real-time assessment; threat assessment; data streams.

DOI: 10.1504/IJEM.2013.059879

International Journal of Emergency Management, 2013 Vol.9 No.4, pp.351 - 365

Available online: 17 Mar 2014 *

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