Search results

Cloud computing promises dependable services offered through next-generation data centres based on virtualization technologies for computation, network and storage. Big Data Applications have been made viable by cloud computing technologies due to the tremendous expansion of data. Disaster management is one of the areas where big data applications are rapidly being deployed. This study looks at how big data is being used in conjunction with cloud computing to increase disaster risk reduction (DRR). This paper aims to explore and review the existing framework for big data used in disaster management and to provide an insightful view of how cloud-based big data platform toward DRR is applied.

A systematic mapping study is conducted to answer four research questions with papers related to Big Data Analytics, cloud computing and disaster management ranging from the year 2013 to 2019. A total of 26 papers were finalised after going through five steps of systematic mapping.

Findings are based on each research question.

A specific study on big data platforms on the application of disaster management, in general is still limited. The lack of study in this field is opened for further research sources.

In terms of technology, research in DRR leverage on existing big data platform is still lacking. In terms of data, many disaster data are available, but scientists still struggle to learn and listen to the data and take more proactive disaster preparedness.

This study shows that a very famous platform selected by researchers is central processing unit based processing, namely, Apache Hadoop. Apache Spark which uses memory processing requires a big capacity of memory, therefore this is less preferred in the world of research.

The purpose of this paper is to discuss how, despite increasing data availability from a wide range of sources unlocks unprecedented opportunities for disaster risk reduction, data interoperability remains a challenge due to a number of barriers. As a first step to enhancing data interoperability for disaster risk reduction is to identify major barriers, this paper presents a case study on data interoperability in disaster risk reduction in Europe, linking current barriers to the regional initiative of the European Science and Technology Advisory Group.

In support of Priority 2 (“Strengthening disaster risk governance to manage disaster risk”) of the Sendai Framework and SDG17 (“Partnerships for the goals”), this paper presents a case study on barriers to data interoperability in Europe based on a series of reviews, surveys and interviews with National Sendai Focal Points and stakeholders in science and research, governmental agencies, non-governmental organizations and industry.

For a number of European countries, there remains a clear imbalance between long-term disaster risk reduction and short-term preparation and the dominant role of emergency relief, response and recovery, pointing to the potential of investments in ex ante measures with better inclusion and exploitation of data.

Modern society is facing a digital revolution. As highlighted by the International Council of Science and the Committee on Data for Science and Technology, digital technology offers profound opportunities for science to discover unsuspected patterns and relationships in nature and society, on scales from the molecular to the cosmic, from local health systems to global sustainability. It has created the potential for disciplines of science to synergize into a holistic understanding of the complex challenges currently confronting humanity; the Sustainable Development Goals are a direct reflectance of this. Interdisciplinary is obtained with integration of data across relevant disciplines. However, a barrier to realization and exploitation of this potential arises from the incompatible data standards and nomenclatures used in different disciplines. Although the problem has been addressed by several initiatives, the following challenge still remains: to make online data integration a routine.

This paper aims to present disaster information and a monitoring system in order to utilize earth observation data in the operative process of early warning, mitigation and management of natural disasters. The system is aimed at integrating earth observation data analysis with modern ICTs including GIS, grid, mobile communication and web technology to support disaster monitoring and to share disaster information during a crisis.

The system development concerned outlining an operative disaster monitoring and management process. The process was derived from actual practices, suggestions and needs of different user groups involved in disaster management. After investigating state‐of‐the‐art ICTs and reviewing the existing tools and databases, a suitable system architecture was designed and a prototype system was implemented, adapting to a proven software development process.

The prototype system implementation demonstrated how satellite‐based data can be used to support disaster management processes. Disaster monitoring requires information system infrastructure that would enable communication and integrate various distributed information sources and services.

The result gives ideas for establishing an operative disaster management process involving local authorities, disaster analysts and the public. The process integrates earth observation data analysis with modern ICTs and improves the methods of early warning. The developed concept can be used as the basis for future development of automated real‐time disaster monitoring.

Disaster management is an issue that has received relatively little attention in libraries, particularly in India. With the growth in digital resources in libraries, it is necessary for librarians to understand and apply the ways of protecting digital data and the related equipment from disaster. The purpose of this paper is to address the issues related to digital data protection in libraries in India. It aims to investigate the perceptions of librarians about the probability of digital disasters happening in their libraries, and to assess the level of digital disaster preparedness among libraries.

A questionnaire based survey of 276 libraries in the Western region of India was conducted to find out the digital data protection measures taken by them. The paper provides an overview and analysis of the general trends in digital data protection in these libraries.

The research results indicate that due to lack of knowledge about handling of digital data, and inadequate digital infrastructure setup in organizations, the chances of loss of digital data are high. However, common measures like taking backup of data manually are mostly followed by all libraries. The paper has identified the trends in protection of digital data, as well as the lacunae, in Indian libraries. Basic guidelines on digital data preservation are also presented in the paper.

The guidelines provided in the paper will be useful to any libraries to take measures for protection of the digital data. The libraries will be able to prepare their digital data protection plan and train the staff accordingly.

This paper is the first to address the issue of digital disaster management in libraries in India. It provides a detailed analysis of digital data protection measures taken by Indian libraries currently.

Disaster management information systems (DMISs) have been proposed in different parts of the world for effective response to a disaster. The purpose of this paper is to: compare design approaches of these DMISs; examine similarities in the design of databases and communication infrastructure; and draw conclusions. Based on the examination of the studies, future opportunities have been identified and discussed.

The studies in the available literature on the designs of automated DMISs have been reviewed in the presented paper to identify similarities in design premise, conceptual design and design considerations.

The examination of the available studies indicates that the research on DMIS has increased significantly in different countries of the world since 2004. Data of baseline information and available resources are required by most of the presented studies, as these data are necessary for effective response to a disaster. The communication infrastructures suggested include local area network, wide area network and satellite communication for better coordination between the responders and different relief agencies at different locations. The connectivity to these networks is possible through Ethernet, Wi-Fi, general packet radio service or satellite.

Although the research on DMIS has increased significantly over the last one decade, the studies are still few in numbers. Similarly, only few of the proposed systems have been developed and tested during a real disaster.

The presented review of available studies provides a holistic view of the proposed DMISs which could be useful to the disaster management authorities.

The paper provides valuable information on the differences in the proposed DMISs. This can help in identifying the gaps for future improvements for increased effectiveness of a DMIS. The future opportunities have also been identified in the presented paper and are discussed.

As stated in the United Nations Global Assessment Report 2022 Concept Note, decision-makers everywhere need data and statistics that are accurate, timely, sufficiently disaggregated, relevant, accessible and easy to use. The purpose of this paper is to demonstrate scalable and replicable methods to advance and integrate the use of earth observation (EO), specifically ongoing efforts within the Group on Earth Observations (GEO) Work Programme and the Committee on Earth Observation Satellites (CEOS) Work Plan, to support risk-informed decision-making, based on documented national and subnational needs and requirements.

Promotion of open data sharing and geospatial technology solutions at national and subnational scales encourages the accelerated implementation of successful EO applications. These solutions may also be linked to specific Sendai Framework for Disaster Risk Reduction (DRR) 2015–2030 Global Targets that provide trusted answers to risk-oriented decision frameworks, as well as critical synergies between the Sendai Framework and the 2030 Agenda for Sustainable Development. This paper provides examples of these efforts in the form of platforms and knowledge hubs that leverage latest developments in analysis ready data and support evidence-based DRR measures.

The climate crisis is forcing countries to face unprecedented frequency and severity of disasters. At the same time, there are growing demands to respond to policy at the national and international level. EOs offer insights and intelligence for evidence-based policy development and decision-making to support key aspects of the Sendai Framework. The GEO DRR Working Group and CEOS Working Group Disasters are ideally placed to help national government agencies, particularly national Sendai focal points to learn more about EOs and understand their role in supporting DRR.

The unique perspective of EOs provide unrealized value to decision-makers addressing DRR. This paper highlights tangible methods and practices that leverage free and open source EO insights that can benefit all DRR practitioners.

This study aims to propose iStage, i.e. an intelligent hybrid deep learning (DL)-based framework to determine the stage of the disaster to make the right decisions at the right time.

iStage acquires data from the Twitter platform and identifies the social media message as pre, during, post-disaster or irrelevant. To demonstrate the effectiveness of iStage, it is applied on cyclonic and COVID-19 disasters. The considered disaster data sets are cyclone Fani, cyclone Titli, cyclone Amphan, cyclone Nisarga and COVID-19.

The experimental results demonstrate that the iStage outperforms Long Short-Term Memory Network and Convolutional Neural Network models. The proposed approach returns the best possible solution among existing research studies considering different evaluation metrics – accuracy, precision, recall, f-score, the area under receiver operating characteristic curve and the area under precision-recall curve.

iStage is built using the hybrid architecture of DL models. It is effective in decision-making. The research study helps coordinate disaster activities in a more targeted and timely manner.

The Sendai Framework for Disaster Risk Reduction 2015–2030 calls for a reduction in disaster mortality, yet measuring mortality remains a challenge due to varying definitions of disaster mortality, the quality, availability and diversity of data sources, generating mortality estimates, and how mortality data are interpreted.

This paper uses five case studies to provide details around some of the complexities involved with measuring disaster mortality and to demonstrate the clear need for accurate disaster mortality data.

The findings highlight the benefits of combining multiple data sources for accurate mortality estimates, access to interoperable and readily available global, national, regional and local data sets, and creating standardized definitions for direct and indirect mortality for easier attribution of causes of death.

Countries should find a method of measuring mortality that works for them and their resources, and for the hazards they face. Combining accurate mortality data and estimates and leadership at all levels can inform policy and actions to reduce disaster mortality, and ultimately strengthen disaster risk reduction in countries for all citizens.

The aim of this study is to explore mortality data collection after a destructive earthquake in Bam, Iran.

This is a case study of mortality data collection using a qualitative approach. The study data were collected through interviews with people involved with data collection and processing in Iran, and an evaluation of routinely collected data.

The results indicated that there were many limitations affecting data collection after the earthquake. These limitations are rooted in basic problems within the existing data collection system and a lack of co‐ordination between the groups collecting data, including national and international aid groups that provided help after the earthquake.

The research is based on a very large and destructive earthquake; the results may be different for other natural disasters such as floods.

The main target group of this study is the decision makers involved with the disaster relief issues at national and international levels. This issue of data collection is imperative for future disaster aid.

The study highlights the problems affecting routine collection of mortality data after a disaster, arising from the scant attention paid to proper documentation.

The purpose of this work is the development of a structured case study design process for developing case studies in humanitarian logistics, in particular for short-term predictable disaster situations like floods and hurricanes. Moreover, useful public sources are presented in order to enable researchers to find relevant data for their case studies more easily.

A structured framework for case study design is set up, splitting the process into different steps and phases.

The framework is applied to an illustrative example, where case studies with different numbers and levels of detail of scenarios are designed based on the three-day forecast for hurricane Harvey in 2017. The corresponding solutions demonstrate the relevance of using as much forecast information as possible in case study building, and in particular in scenario design, in order to get useful and appropriate results.

The case study design process is mostly suitable for short-term predictable disasters, but can also be adapted to other types of disasters. The process has been applied to one specific hurricane here which serves as an example.

Also for practitioners, the results of this work are highly relevant, as constructing realistic cases using real data will lead to more useful results. Moreover, it is taken into account in the case study design process that relief agencies are regularly confronted with disasters in certain areas and hence need to define the basic planning situation and parameters “once and for all” and on a long-term basis, whereas disaster specific data from forecasts are only available within a short time frame.

The new design process can be applied by researchers as well as practitioners, and the publicly available data sources will be useful to the community.