BIG DATA APPLICATIONS FOR DISASTER MANAGEMENT

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.