Disaster Recovery Research Articles (Page 1)

ABSTRACT This study examines the efficiency of three segmentation algorithms – Felzenszwalb, Simple Non-Iterative Clustering (SNIC), and Multiresolution Segmentation (MRS) – in conjunction with four deep learning models (2D CNN, 3D CNN, Hybrid CNN, and Vision Transformer (ViT)) for evaluating building damage after the 2020 Beirut port explosion. To validate the generalizability of the models, an additional case study was conducted using the large-scale xBD dataset. Utilizing WorldView-2 imagery for the Beirut case, the segmentation performance and classification accuracy were assessed across four damage categories: destroyed, major damage, minor damage, and no damage. The methodology first divides the pre-disaster image into segments using these three algorithms. Subsequently, image classification was performed on the stacked pre- and post-disaster image patches. The final classification is performed using decision-level fusion, which is based on the majority vote of all pixels classified by the deep learning models within the image objects obtained from segmentation. The segmentation results showed that SNIC provided the most balanced performance, achieving the highest F1-score (0.5589). Regarding damage classification for the Beirut dataset, the Vision Transformer (ViT) significantly outperformed all CNN variants. The combination of ViT with SNIC achieved the highest overall accuracy of 95.1% and an overall F1-score of 0.950. This approach yielded superior class-wise F1-scores, reaching 0.978 (destroyed), 0.943 (major), 0.957 (minor), and 0.945 (No Damage). On the xBD dataset, the ViT model confirmed its robustness by achieving the highest accuracy (81.4%) and F1-score (0.789), outperforming the CNN models and other state-of-the-art benchmarks. These results emphasize the efficacy of the ViT combined with SNIC for quick, accurate, and generalizable damage assessments, providing essential insights for disaster management and recovery efforts.

ABSTRACT This paper examines how political agendas across the ideological spectrum compromise ethical principles in emergency management in the United States. Through a literature review and comparative fact‐based scenario analysis, it demonstrates that both conservative and liberal governments have politicized disaster declarations, resource distribution, and recovery processes, often in direct violation of professional ethics codes, including those of the International Association of Emergency Managers (IAEM) and the Department of Homeland Security (DHS). Examples include selective disaster declarations driven by electoral incentives, inequitable funding allocations, and the strategic minimization of climate‐related risks. Drawing on the core principles of neutrality, impartiality, and humanity, aligned with both emergency management ethics and international humanitarian standards, this study highlights how bipartisan political pressures systematically erode public trust and fairness in disaster response and recovery. The findings argue for the depoliticization of disaster management processes and propose actionable strategies to reinforce ethical integrity across government. This analysis is critical for advancing a more transparent, equitable, and accountable disaster management framework in an increasingly polarized political landscape. Related Articles Ash, J. 2010. “New Nuclear Energy, Risk, and Justice: Regulatory Strategies for an Era of Limited Trust.” Politics & Policy 38, no. 2: 255–284. https://doi.org/10.1111/j.1747‐1346.2010.00237.x . Belligoni, S. 2024. “Held in the Grip: Political Status, Governing Institutions, and Emergency Management Procedural Arrangements in the Cases of Florida and Puerto Rico.” Politics & Policy 52, no. 2: 349–354. https://doi.org/10.1111/polp.12581 . Liu, Z., and Z. Zhu. 2023. “China's Pathway to Domestic Emergency Management: Unpacking the Characteristics in System Evolution.” Politics & Policy 49, no. 3: 619–650. https://doi.org/10.1111/polp.12407 .

Disaster Recovery as Code: Building Self-Healing Financial Infrastructures in the Public Cloud

The open, shared, and virtualized nature of the cloud also introduces numerous security threats, which posing significant challenges to big data security in enterprises. Through case studies and comparative methods, this paper systematically identifies core security issues related to big data in cloud environments, including data breaches, unauthorized access, cloud platform vulnerabilities, and cross-border data compliance dilemmas. This paper constructs a tripartite integrated security framework encompassing technology, management, and compliance: at the technical level, end-to-end encryption, multi-factor authentication, and zero-trust architecture are adopted; at the management level, a full lifecycle security management mechanism is established, incorporating the principle of least privilege, regular assessments, disaster recovery, and training; at the compliance level, adherence to relevant domestic and international regulations is emphasized, with clear requirements for data sovereignty and cross-border data transfer. The research recommends that enterprises develop a security governance framework integrating "defense-in-depth, continuous optimization, and shared responsibility." By strengthening technical measures, optimizing management practices, and ensuring regulatory compliance, enterprises can comprehensively enhance the confidentiality, integrity, and availability of data in cloud environments, thereby providing a secure and reliable foundation for their digital transformation.

The National Response Framework (NRF) provides a guideline on how the USA responds to disasters and emergencies.1 The NRF is designed as a tiered response such that incidents are handled at the lowest level. It states that it is built on scalable, flexible and adaptable concepts identified in the National Incident Management System.2 Globalisation, whether by its inherent structure or the effect of the disaster itself, no longer geographically limits the extent of today's disasters. Climate change, international economic integration, supply chain infrastructure, cyberattacks, infectious diseases, ease of travel and various national political interests change the way the NRF needs to address disaster preparedness, response, recovery and mitigation strategies. While the NRF approach begins at the lowest level, there needs to be simultaneous preparation at the highest levels, recognising that the impacts of disasters, as in much of the rest of the world, are no longer limited by borders. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/ business/.

Cloud computing has transformed the way organizations approach IT infrastructure and service delivery by enabling on-demand access to computing resources over the internet. Rather than investing heavily in physical servers, storage and networking up front, businesses can adopt a pay-as-you-go model that shifts the major expense from capital expenditure (CapEx) to operational expenditure (OpEx). This change not only reduces upfront cost and financial risk, but also enables more flexible budgeting and allocation of resources. One of the most important benefits is scalability and elasticity: organizations can rapidly scale resources up or down in response to demand fluctuations such as seasonal peaks, sudden project-bursts or variable workload patterns without having to over-provision infrastructure in advance. This sensitivity to demand leads to more efficient resource utilization and better cost management. At the same time, cloud platforms enhance agility: new services or applications can be deployed quickly because the underlying infrastructure is already available, allowing businesses to test ideas, iterate faster and accelerate time-to-market. Cloud computing also opens the door to advanced technologies that might otherwise be out of reach such as artificial intelligence (AI), machine learning (ML), predictive analytics and large-scale data processing. Because cloud providers offer powerful compute, storage and analytics platforms, organizations can leverage these capabilities without large upfront hardware investments. This contributes to innovation and strategic differentiation. Accessibility and collaboration are further improved: cloud-based tools and services make it easier for users and teams to access applications and data anytime, anywhere, and from multiple devices, which supports remote work, cross-location projects and flexible operations. Security, disaster recovery and business continuity are additional advantages. Many cloud providers offer robust infrastructure with redundancy, backup, and disaster-recovery solutions built-in. This means that organizations can improve their resilience to hardware failures, site outages or data loss without bearing all the costs themselves. Moreover, cloud providers typically maintain dedicated security expertise and compliance capabilities, which can strengthen an organization’s security posture although responsibility for configuration and governance remains shared. Finally, cloud computing also contributes to sustainability efforts: by leveraging shared infrastructure, optimizing energy usage and reducing the footprint of individual data centers, organizations can align their IT operations with environmental goals and corporate social responsibility.

This article critically examines the disaster literature from the past three years (2022-2025) to evaluate the relationship between place attachment and children's experience of disaster response and recovery. Place attachment offers a systematic lens through which we comprehensively map our understanding of the factors that shape, and are shaped by, lived experience of disaster amongst children. We outline why specific consideration of children's health and wellbeing is significant through this lens, and further consider place attachment in relation to factors identified across relevant bodies of literature. Findings are synthesized across three interdependent, cyclical dimensions: (1) disaster context, including type, location, infrastructure, and planning, (2) children's holistic experiences of place attachment, including emotional, physical, cultural, and identity-based connections; and (3) disaster outcomes such as displacement, recovery, and rebuilding. We propose suggestions for future research, particularly emphasizing the need for an expanded evidence-based, conceptual framework that builds on the model presented in this paper. Not applicable.

RoamML distributed continual learning: Adaptive and flexible data-driven response for disaster recovery operations

In the contemporary digital environment, the importance of resilient and adaptable network infrastructures cannot be overstated. This research examines the efficacy of Software-Defined Networking (SDN), Network Functions Virtualization (NFV), and hybrid models in constructing robust networks capable of swift recovery from disasters and network outages. Utilizing the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), a comprehensive evaluation is undertaken to gauge the performance of different network infrastructures based on crucial criteria including fault recovery time, scalability, cost-effectiveness, implementation complexity, latency, and security considerations. The findings from the TOPSIS analysis highlight NFV with Dynamic Scaling as the most effective infrastructure, particularly excelling in scalability, cost-efficiency, and fault recovery. Following closely, the Hybrid SDN-NFV Architecture offers a balanced approach by harnessing the strengths of both SDN and NFV technologies, securing the second position. SDN with Redundancy ranks third, demonstrating notable strengths in scalability, cost-efficiency, and latency. While Traditional Network Infrastructure and Cloud-based Network Virtualization are respectively cost-effective and secure, they exhibit limitations in fault recovery, scalability, and cost-effectiveness.

This study investigates the spatial-cognitive representations, emotional responses, levels of disaster preparedness, and institutional expectations of fifth-grade middle school students in Bağcılar, a district of Istanbul that exemplifies urban life under high seismic hazard in Türkiye. Adopting a qualitative case study design, the research employed semi-structured interview forms to collect data, which were subsequently analyzed through content analysis. The results of the study reveal that students exhibit a vivid awareness of earthquake risks; however, this awareness is closely interwoven with intense feelings of anxiety, uncertainty, and perceived inadequacy. Rather than framing earthquakes as geophysical phenomena, students predominantly conceptualize them through their destructive impacts—such as loss of life and property, physical damage to the built environment—and the emotional responses they trigger, including fear, panic, and helplessness. Furthermore, students view disaster response and recovery not merely as matters of individual preparedness, but also as requiring institutional capacity, coordinated governance, and systematic risk-reduction measures led by authorities.

Mary Shelley's dystopian novel The Last Man (1826), while a cornerstone of literary studies, offers a profound and underutilized historical imaginary for understanding contemporary societal crises. This article moves beyond traditional literary criticism to reframe Shelley's work as a sociological and educational case study, particularly relevant in the post-COVID-19 era. Using a theoretical framework grounded in the sociology of collective trauma and social cohesion, this analysis investigates the novel's depiction of a "narrative plague"—the collapse of shared meaning, social bonds, and institutional trust—that runs parallel to the medical pandemic it portrays. The study employs a qualitative textual analysis to examine the social-psychological dimensions of isolation, loss, and anomie as depicted in the novel. Findings reveal how the narrative simulates the disintegration of social capital and the failure of political and scientific institutions to manage catastrophe, offering critical insights into the foundations of community resilience and mental health. This 19th-century text is analyzed as a pre-sociological thought experiment, connecting historical representations of societal collapse with contemporary social science discourse on disaster response and recovery. The article argues that the novel's themes have direct implications for educational policy and pedagogy, highlighting the urgent need for trauma-informed approaches that can rebuild social inclusion, foster critical media literacy, and cultivate psychological resilience in an era of profound informational and social fragmentation.

Wireless Sensor Networks (WSNs) are vital to a wide range of applications, spanning from environmental monitoring to safety-critical systems. Ensuring dependable operation in these networks critically depends on selecting an optimal node scheduling algorithm; however, this remains a major challenge since no single approach performs best under all conditions. To address this issue, this paper proposes an AI-driven framework that evaluates scenario-specific functional requirements—such as coverage, connectivity, and network lifetime—to identify the optimal node scheduling algorithm from a pool that includes Hidden Markov Models (HMMs), BAT, Bird Flocking, Self-Organizing Maps (SOFMs), and Long Short-Term Memory (LSTM) networks. The framework was evaluated using a neural network trained on simulated data and tested across five real-world scenarios: healthcare monitoring, military operations, industrial IoT, forest fire detection, and disaster recovery. The results clearly demonstrate the effectiveness of the proposed framework in identifying the most suitable algorithm for each scenario. Notably, the LSTM algorithm frequently achieved near-optimal performance, excelling in critical objectives such as network lifetime, connectivity, and coverage. The framework also revealed the complementary strengths of other algorithms—HMM proved superior for maintaining connectivity, while Bird Flocking excelled in extending network lifetime. Consequently, this work validates that a scenario-aware selection strategy is essential for maximizing WSN dependability, as it leverages the unique advantages of diverse algorithms.

Domain Name System (DNS) serves as a vital Internet component, which converts friendly domain names into their corresponding computer language IP addresses. Network service availability suffers from several cyber threats in DNS systems because Distributed Denial of Service (DDoS) attacks, spoofing, and cache poisoning expose data to unauthorized access and reduce service availability. The research examines virtualization technology, which serves as a DNS security enhancement solution to increase system resilience capacity. This work implements DNS security enhancements through virtualization elements that include threat isolation with service segmentation as well as automated recovery services with dynamic resource allocation to protect DNS systems against vulnerabilities. The framework demonstrated improvements through real-world deployment with case studies and simulations because it provided 98% improved service accessibility during DDoS attacks and decreased disaster recovery time by 60% at the same time as decreasing operational costs by 30%. The study displays extensive proof demonstrating that virtualization functions as a fundamental delivery method for fault tolerance as well as enables superior protection against preventing complex security threats and scalability features. The research findings demonstrate that DNS component protection together with fast disaster recovery capability receives vital security features from virtualization implementation. Security-conscious organizations plagued by evolving threats should adopt virtualization-based DNS service maintenance because it offers scalable and price-efficient delivery capabilities. Virtualization in DNS demonstrates itself as a strategic forward-thinking approach to create sustainable yet flexible protected online structures.

Environmental health practitioners (EHPs) are expected to possess critical competencies in disaster risk reduction, emergency response and recovery, which should ideally be developed during undergraduate education. However, since the transition in 2016 from diploma to degree programmes in environmental health in South African higher institutions, there has been limited insight into the adequacy of disaster management training in these programmes. This study aimed to analyse the training content, course structures and competency frameworks currently employed to prepare EHPs in South Africa for roles in disaster management. An exploratory concurrent mixed-methods design examined the current structure and delivery of disaster management modules in environmental health programmes across South Africa. A semi-structured self-administered questionnaire was used to elicit information on the relevance of disaster management in environmental health, course delivery, competency approaches and work-integrated learning. Findings revealed systemic inconsistencies and critical gaps in curriculum implementation. These challenges included the lack of module duration, structure standardisation and the limited integration of work-integrated learning and digital learning methodologies. The study concludes that a standardised national curriculum is needed that is aligned with the South African Disaster Management Framework, the EHP professional scope of practice and the Sendai Framework for Disaster Risk Reduction.ContributionThe study recommends establishing a Disaster Management Academics Forum to improve curriculum consistency, encourage academic collaboration and promote ongoing quality enhancement. These measures are essential for maintaining uniform graduate skills, which will strengthen the professional capacity of EHPs as frontline workers in disaster risk reduction and further reinforce South Africa’s long-term resilience.

Two legacies of colonialism, climate change and the gender order, have left their imprint on the problems the world faces. Greenhouse gas emissions from burning fossil fuels, deforestation and industrial agriculture have warmed the planet, triggering both increases in immediate ‘natural’ disasters and the imminent juggernaut of climate change. Fuel for industrial development came from exploitation of the Global South as colonial powers sought cheap natural resources, land and labour. Indonesia’s colonial history is a prime example of the vast wealth that was harvested to support northern industry; subsequently, it has been harnessed for postcolonial economic development. Colonial rule also transformed gender relations, resulting in a patriarchal postcolonial New Order state that deployed gender ideology to advance its development agenda, linking the exploitation of women to the exploitation of the environment. This paper explores the ways that colonial legacies have structured the exploitation of gender and the environment and whether and how it is possible to find a postcolonial agenda for a better future. The lessons to be learned from studying disaster recovery in Indonesia and elsewhere are key to addressing climate change. A riskscape model of disaster and climate change that focuses on gender relations provides a means to analyse and manage both with the ultimate goal of finding transformative resilience.

Abstract: Within the modern advanced time, the danger of disasters—both normal and man-made—poses noteworthy dangers to organizational information judgment and operational progression. A novel concept called Catastrophe Recuperation as a Cloud Benefit (DRaaS) employments cloud computing to supply calamity recuperation arrangements that are reasonable and versatile. This consider analyzes the center thoughts of DRaaS, surveys the body of investigate and observational prove, and looks into the troubles and real-world employments of the innovation. The consider assesses the adequacy of DRaaS arrangements and looks at organizational appropriation patterns employing a mixed-method approach. Although it seems that DRaaS essentially reduces costs and speeds up recovery, problems with security, integration, and compliance persist. The primary focus areas of the proposals are future bearing research and the best practices for a successful DRaaS installation. The increasing dependence on digital infrastructure has heightened the need for efficient disaster recovery (DR) methods. Disaster Recovery as a Cloud Service (DRaaS) is researched here as an elastic, cost-saving substitute for conventional DR solutions. It is aimed at investigating how DRaaS can help industries reduce downtime and data loss, with a particular emphasis on small and medium-sized businesses (SMEs). The research utilizes a mixed-methods design, integrating comparative evaluation of cloud-based and on-premises DR models alongside case studies of organizations using DRaaS. Recovery time objective (RTO), recovery point objective (RPO), cost-effectiveness, and system robustness are measured through simulation and user-reported values. Outcomes show that DRaaS decreases RTO and RPO dramatically when compared to traditional systems, while providing increased flexibility and less capital investment. SMEs especially enjoy the pay-as-you-go approach and automated failover options. Concerns regarding data sovereignty and vendor lock-in are still significant challenges. Finally, DRaaS appears to be an efficient and feasible solution for disaster recovery in the contemporary era, particularly for companies requiring agility and cost savings. The publication recommends best practices for DRaaS implementation, including careful vendor scrutiny, compliance alignment, and hybrid deployment strategies.

This whitepaper presents a comprehensive analysis of multi-cloud data synchronization patterns for Microsoft Dynamics 365 Finance and Operations (D365 F&O) integrated with Amazon Web Services (AWS) and Google Cloud Platform (GCP). As organizations increasingly adopt multi-cloud strategies to optimize performance, reduce vendor lock-in, and enhance disaster recovery capabilities, the complexity of maintaining data consistency across distributed cloud environments has become a critical challenge. Our research examines four primary synchronization patterns: real-time synchronization using OData APIs and Business Events, batch processing through the Data Management Framework (DMF), event-driven architectures leveraging cloud-native messaging services, and hybrid approaches that intelligently route data based on criticality and volume requirements. Through analysis of implementation case studies across e-commerce, manufacturing, and financial services sectors, we demonstrate that hybrid synchronization patterns achieve optimal performance with 99.9% data consistency while reducing operational costs by up to 40%. Key findings include: (1) Real-time patterns excel for transactional data with sub-second latency requirements but face API throttling limitations of 200 requests per minute; (2) Batch processing patterns efficiently handle high-volume data transfers but introduce latency measured in hours; (3) Event-driven patterns provide superior fault tolerance and scalability through loose coupling; and (4) Hybrid patterns optimize resource utilization by matching synchronization method to data characteristics. This research contributes to the body of knowledge on enterprise multi-cloud integration by providing practical implementation guidance, performance benchmarks, and security frameworks that organizations can leverage to design robust, scalable, and secure multi-cloud data synchronization architectures.

Contemporary enterprise data strategies increasingly embrace multi-cloud architectures to leverage best-of-breed services while mitigating vendor lock-in risks. Microsoft Dataverse, serving as a central data platform within the Microsoft ecosystem, presents unique opportunities and challenges when extended across heterogeneous cloud environments. This research examines the performance characteristics, architectural patterns, and benchmarking methodologies essential for successful Dataverse cross-cloud replication implementations. Our analysis encompasses three primary replication patterns: Active-Passive configurations for disaster recovery, Active-Active deployments for global distribution, and Data Lake consolidation for unified analytics. Through comprehensive performance evaluation using industry-standard metrics including replication latency, throughput, data consistency, and cost efficiency, we establish baseline benchmarks and identify optimization strategies for enterprise implementations. Key findings demonstrate that while cross-cloud replication introduces complexity challenges, organizations can achieve sub-100ms latency targets and maintain 99.9% data consistency through properly architected solutions. Cost optimization strategies, including intelligent data routing and tiered storage utilization, can reduce total cost of ownership by 25-30% compared to naive replication approaches. Security and compliance frameworks, when properly implemented, enable organizations to maintain regulatory adherence across jurisdictional boundaries while supporting global data accessibility requirements.

Hurricane Helene made landfall in late September 2024, causing widespread damage to Western North Carolina. In this interview, Dr. Susan Mims, CEO of Dogwood Health Trust, discusses her organization’s response to Hurricane Helene and its role in disaster recovery in Western North Carolina. Dr. Mims describes the role that philanthropy can play in disaster response, and further emphasizes the importance of collaboration, flexibility, and a sustained focus on long-term recovery efforts.

Who's at the table? Disaster committee composition and policy pathways after Hurricane Sandy.