A Big Data Reference Architecture for Emergency Management

Iglesias, Carlos Á.; Favenza, Alfredo; Carrera, Álvaro

doi:10.3390/info11120569

Cited by 17 publications

(7 citation statements)

References 109 publications

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“…Of these, the majority of work is generalised dialogue and articles focused on organisational mindfulness (Amaye et al, 2016 ) and group privacy (Gerdes, 2020 ). Also, it is worth noting the work of Iglesias et al ( 2020 ) on building reference architecture for big data, as well as critical components required for the system. Furthermore, the authors highlighted the potential uses of big data in each crisis phase for a variety of tasks based on the core capabilities developed by the National Response Framework.…”

Section: Resultsmentioning

confidence: 99%

A systematic literature review on the use of big data analytics in humanitarian and disaster operations

2022

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At the start of this review, 168 million individuals required humanitarian assistance, at the conclusion of the research, the number had risen to 235 million. Humanitarian aid is critical not just for dealing with a pandemic that occurs once every century, but more for assisting amid civil conflicts, surging natural disasters, as well as other kinds of emergencies. Technology's dependability to support humanitarian and disaster operations has never been more pertinent and significant than it is right now. The ever-increasing volume of data, as well as innovations in the field of data analytics, present an incentive for the humanitarian sector. Given that the interaction between big data and humanitarian and disaster operations is crucial in the coming days, this systematic literature review offers a comprehensive overview of big data analytics in a humanitarian and disaster setting. In addition to presenting the descriptive aspects of the literature reviewed, the results explain review of existent reviews, the current state of research by disaster categories, disaster phases, disaster locations, and the big data sources used. A framework is also created to understand why researchers employ various big data sources in different crisis situations. The study, in particular, uncovered a considerable research disparity in the disaster group, disaster phase, and disaster regions, emphasising how the focus is on reactionary interventions rather than preventative approaches. These measures will merely compound the crisis, and so is the reality in many COVID-19-affected countries. Implications for practice and policy-making are also discussed.

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Section: Resultsmentioning

confidence: 99%

A systematic literature review on the use of big data analytics in humanitarian and disaster operations

2022

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“…The absence of a clear and general understanding of the concepts and facts that significantly increase awareness and capacity building is considered to be one of the most significant barriers to usage of DTSCs to manage disasters. This may be partially ascribed to the problem's inherent complexity and the wide range of spatial and temporal scales on which its manifestations take place [176].…”

Section: Quality Quantity Ambiguity and Complexity Of Available Datamentioning

confidence: 99%

Digital Twin Smart Cities for Disaster Risk Management: A Review of Evolving Concepts

Ariyachandra

Wedawatta

2023

Sustainability

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Natural hazard-induced disasters have caused catastrophic damage and loss to buildings, infrastructure, and the affected communities as a whole during the recent decades and their impact is expected to further escalate in the future. Thus, there is a huge demand for disaster risk management using digitalisation as a key enabler for effective and efficient disaster risk management systems. It is widely accepted that digital and intelligence technologies can help solve key aspects of disaster risk management such as disaster prevention and mitigation, and rescue and recovery. Digital Twin (DT) is one of the most promising technologies for multi-stage management which offers significant potential to advance disaster resilience. Smart Cities (SCs) use pervasive information and communications technology to monitor activities in the city. With increasingly large applications of DTs combined with big data generated from sensors in a SC, it is now possible to create Digital Twin Smart Cities (DTSCs). Despite the increasing prevalence of DTSC technologies and their profound impact on disaster risk management, a systematic and longitudinal view of the evolution to the current status of DTSC for disaster risk management does not exist. This review analyses 312 titles and abstracts and 72 full papers. To begin with, a scientific review of DT and SC is undertaken, where the evolution of DTSCs is reviewed. In addition, the intelligence technologies used in DTSCs for disaster risk management are assessed and their benefits are evaluated. Furthermore, the evolution and technical feasibility of DTSC-driven disaster risk management is evaluated by assessing current applications of DTSCs in disaster risk management. It was found that despite the significant potential benefits offered by DTSCs, they also add a new layer of complexities and challenges inherent to these technologies to the already complex web of complexities involved in disaster risk management. These challenges can be addressed by understanding how the process of utilising DTSCs in disaster risk reduction and sustainability is designed, which is essential for comprehending what DTSCs may offer, how it is implemented, and what it means to all involved stakeholders. This paper contributes to the knowledge by improving the understanding of the current status of DTSC technologies and their impact on disaster risk management, and articulating the challenges in implementing DTSC, which inspires the professional community to advance these technologies to address them in future research.

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“…Nowadays when any kind of disaster occurs, the related type of data i.e., photos, videos, text, audio, etc., can easily be accessedfrom social media, as social media is a primary mode ofinstant communication. Organizations like disaster management systems and emergency response systems validate such types of data based on the validation services [1]. Most of the data available on social media is irrelevant and redundant.…”

Section: Introductionmentioning

confidence: 99%

Assessing Long-Term Impacts of Disaster Using Predictive Data Analyticsfor Effective Decision Support

Mishra

2023

ijarcs

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Disaster is a big issue that seriously disrupts and affects the community or society. The impact of a disaster causes a short and long period of time. To analyze the impacts of disasters there are lots of available related datasets. Data analytics methods have the potential to assess the impacts of different types of disasters. Collectively data analytics and machine learning techniques play an important role in transforming and being able to make decisions about our social, economic, mental, and psychological things. The objective of this paper is to assess the impacts of disasters from immediate term to long-term, provide crucial help to the emergency management workforce, and policy decisions making based on the latest available datasets. With the help of the various data agencies, extraction of information and activities carried out, we can determine the effects on disaster victims, their community and impacts on society in general. The analysis provides the statistics that can guide our emergency service about the status of facilities that can further support the survivors, and other related information. Detailed assessment i.e., structural survey and hazard mapping provide specific information about reconstruction and mitigation to monitor the situation, needs of the victims, and supporting entities. The assessment is based on the type of disaster that happened and its impact after a few years.In the current technological advancement of data analytics and machine learning algorithms, the prediction of the long-term effects of a disaster can be performed. Analyzing the impacts over a long period of time is also dependent on the growth of actively cared datasets gathering bodies like agencies, government, NGOs, media, etc., where prediction of short-term and long-term impacts is dependent on the available datasets. Available datasets are preprocessed using data analytics tools and implementation of training and testing for the purpose of predictions and recommendations. As a huge amount of data sets are available through different sources the classification of the datasets can also be performed resulting fast and accurate processing. Model validation techniques play an important role to check the validation, test result, and related outcomes. In this paper advanced machine learning and data analytics tools i.e., XG boost, modified SVM, and modified RF are used for better prediction. The analysis of the short-term effects of disasters has already been suggested and recommended by the various conventional approaches. Here the focus is to analyze and detect the long-term effects of a disaster along with recommendations and models preparing for good decision-making. Therefore, planning should be focused on assessing the impacts from short-term to long-term. The findings of the paper would be helpful to the agencies, local & national authorities, and the government by recommending action plans and their future effects for a longer period in case of disaster.

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A Big Data Reference Architecture for Emergency Management

Cited by 17 publications

References 109 publications

A systematic literature review on the use of big data analytics in humanitarian and disaster operations

A systematic literature review on the use of big data analytics in humanitarian and disaster operations

Digital Twin Smart Cities for Disaster Risk Management: A Review of Evolving Concepts

Assessing Long-Term Impacts of Disaster Using Predictive Data Analyticsfor Effective Decision Support

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