A Review on Applications of Big Data for Disaster Management

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, improved comfort, usability and endurance based on the integration of energy consumption data, environmental conditions and levels of occupancy. This paper aims 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.

Big Data and Its Applications in Smart Real Estate and the Disaster Management Life Cycle: A Systematic Analysis

“…Since then, it is a norm to use technology for disaster risk management and response. Various research studies were done on analyzing the sentiments of people at the time of disaster to identify their needs during the crisis [19,122,162,[164][165][166]. Advanced methods of satellite imagery, machine learning, and predictive analysis are applied to gather information regarding any forthcoming disaster along with its consequences.…”

“…Boakye et al proposed a framework that uses big data analytics to predict the results of a natural disaster in the society [162]. Machine learning and image processing also provide heat maps of the affected area, which are helpful in providing timely and quick aid to affected people [166]. Table 16 shows the uses of big data for disaster risk management, as well as the phases and features of big data.…”

Review of Big Data and Processing Frameworks for Disaster Response Applications

“…Essentially, it deals with the management of resources and information towards a disastrous event and is measured by how efficiently, effectively and seamlessly one coordinates these resources [15]. Therefore, the disaster management cycle comprises four phases divided into two groups, namely risk management (mitigation and preparedness) and crisis management (response and recovery) [12]. Mitigation phase is designed to reduce or eliminate risk, preparedness deals with planning the response to a disaster, response aims at maintaining or reestablishing public safety, and recovery consists on restoring the living conditions in the affected areas.…”

“…To overcome such challenges, the big data paradigm provides data management tools and techniques for storage, processing and security the data. Therefore, the Big data paradigm can be divided into four main application areas: (i) big data methods, which focus on uncovering hidden trends and patterns from collection of data; (ii) big data storage, which deals with database management systems to store data; (iii) processing, which provides data processing frameworks to do batch and stream processing; and (iv) representation, which provides visualizations based on the processed data [12]. This study focuses on the third application area (big data processing frameworks).…”

“…Arslan et al [12], in addition to the disaster phases, considered the disaster type (floods, earthquakes, fire, hurricanes, and smog), data used (geoinformatics and sensor data, seismic intensity data, gas sensor data, Global Positioning System (GPS) traces, social media data, temperature data, population distribution data, video and socio-economic data), and key technologies used (Geographical Information Systems (GIS), cloud server, Mysql databases, NoSQL databases, Hadoop, ZigBee, Hive, Storm, and semantic networks) in each of twelve cases studies. Arslan et al [12] found that there is a variety of big data available for each phase of disaster management and that linking different datasets with different kind of disasters is a big challenge. Recently, Yu et al [13] presented a systematic literature review based on 149 selected articles from 101 journal articles.…”