Big Data Applications for Disaster Management

Muhammad Arslan (), Ana-Maria Roxin (), Christophe Cruz () and Dominique Ginhac ()
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Muhammad Arslan: Le2i - Laboratoire d'Electronique, d'Informatique et d'Image [EA 7508] - UTBM - Université de Technologie de Belfort-Montbeliard - UB - Université de Bourgogne - ENSAM - École Nationale Supérieure d'Arts et Métiers - CNRS - Centre National de la Recherche Scientifique
Ana-Maria Roxin: Le2i - Laboratoire d'Electronique, d'Informatique et d'Image [EA 7508] - UTBM - Université de Technologie de Belfort-Montbeliard - UB - Université de Bourgogne - ENSAM - École Nationale Supérieure d'Arts et Métiers - CNRS - Centre National de la Recherche Scientifique
Christophe Cruz: Le2i - Laboratoire d'Electronique, d'Informatique et d'Image [EA 7508] - UTBM - Université de Technologie de Belfort-Montbeliard - UB - Université de Bourgogne - ENSAM - École Nationale Supérieure d'Arts et Métiers - CNRS - Centre National de la Recherche Scientifique
Dominique Ginhac: Le2i - Laboratoire d'Electronique, d'Informatique et d'Image [EA 7508] - UTBM - Université de Technologie de Belfort-Montbeliard - UB - Université de Bourgogne - ENSAM - École Nationale Supérieure d'Arts et Métiers - CNRS - Centre National de la Recherche Scientifique

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Abstract: The term "disaster management" comprises both natural and man-made disasters. Highly pervaded with various types of sensors, our environment generates large amounts of data. Thus, big data applications in the field of disaster management should adopt a modular view, going from a component to nation scale. Current research trends mainly aim at integrating component, building, neighborhood and city levels, neglecting the region level for managing disasters. Current research on big data mainly address smart buildings and smart grids, notably in the following areas: energy waste management, prediction and planning of power generation needs (based on smart meter readings, statistical learning tools, integration of renewable energy sources, open service clouds), dynamic energy management (based on real-time data reading, benchmarking, visualization and optimization), and improved comfort, usability and endurance (based on the integration of energy consumption data, environmental conditions and levels of occupancy). However, the existing literature on big data for disaster management is limited. This papers aims to address this gap by presenting a systematic literature review on the applications of big data in disaster management. The paper will first presents the visual definition of disaster management and describes big data; it will then illustrate the findings and gives future recommendations after a systematic literature review.

Keywords: Cloud; disaster management; big data; disasters; sensor data; Challenges; Networks (search for similar items in EconPapers)
Date: 2017-05-04
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Citations: View citations in EconPapers (1)

Published in 16th International Conference on Informatics in Economy (IE 2017): Education, Research and Business Technologies, May 2017, Bucarest, Romania

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-01858265

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