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Disasters and armed conflict often co-occur, but does that imply that disasters trigger or fuel conflict? In the small but growing body of literature attempting to answer this question, divergent findings indicate the complex and contextual nature of a potential answer to this question. The purpose of this study is to contribute a robust cross-country analysis of the co-occurrence of disaster and conflict, with a particular focus on the potential role played by disaster.

Grounded in a theoretical model of disaster–conflict co-occurrence, this study merges data from 163 countries between 1990 and 2017 on armed conflict, disasters and relevant control variables (low human development, weak democratic institutions, natural resource dependence and large population size/density).

The main results of this study show that, despite a sharp increase in the co-occurrence of disasters and armed conflict over time, disasters do not appear to have a direct statistically significant relation with the occurrence of armed conflict. This result contributes to the understanding of disasters and conflicts as indirectly related via co-creation mechanisms and other factors.

This study is a novel contribution, as it provides a fresh analysis with updated data and includes different control variables that allow for a significant contribution to the field.

The study, within the Increasing Resilience of Cultural Heritage (ResCult) project, aims to support civil protection to prevent, lessen and mitigate disasters impacts on cultural heritage using a unique standardised-3D geographical information system (GIS), including both heritage and risk and hazard information.

A top-down approach, starting from existing standards (an INSPIRE extension integrated with other parts from the standardised and shared structure), was completed with a bottom-up integration according to current requirements for disaster prevention procedures and risk analyses. The results were validated and tested in case studies (differentiated concerning the hazard and type of protected heritage) and refined during user forums.

Besides the ensuing reusable database structure, the filling with case studies data underlined the tough challenges and allowed proposing a sample of workflows and possible guidelines. The interfaces are provided to use the obtained knowledge base.

The increasing number of natural disasters could severely damage the cultural heritage, causing permanent damage to movable and immovable assets and tangible and intangible heritage. The study provides an original tool properly relating the (spatial) information regarding cultural heritage and the risk factors in a unique archive as a standard-based European tool to cope with these frequent losses, preventing risk.

The purpose of this study is to review the role of knowledge management (KM) in disaster management and crisis. Disaster causes many detrimental impacts on human lives through loss of life and damage to properties. KM has been shown to dampen the impact of the disaster on the utilization of knowledge among agencies involved and the local communities impacted by disasters.

Through a bibliometric methodology (co-citation, bibliographic coupling and co-word analysis), this study presents significant themes in the past, current and future predictions on the role of KM in disaster management. In this review paper, 437 publications were retrieved from the Web of Science and analyzed through VOSviewer software to visualize and explore the knowledge map on the subject domain.

Findings suggest that the significant themes derived are centralized to disaster preparedness during disaster and disaster postrecovery. This review presents a state-of-art bibliometric analysis of the crucial role of KM in building networks and interconnection among relevant players and stakeholders involved in disaster management.

The main implication of this study is how the authorities, stakeholders and local community can integrate the KM system within the three stages of disasters and the crucial role of technologies and social media in facilitating disaster management.

To the best of the authors’ knowledge, this is the first study to present a bibliometric analysis in mapping KM’s past, present and future trends in disaster management.

This paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.

Automated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.

Validation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.

Our attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.

Improving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.

Our literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.

Unlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.

Infrastructures become critical with the emerging threats triggering through disasters. Sri Lanka is a country with a higher risk of disaster impacts, in which the eye-opening has widened towards mitigating the damages towards critical infrastructures. Based on this, the purpose of this paper is to develop an index that identifies the significance of critical infrastructure resilience.

From the initial literature survey, disaster resilience is defined as capacity of three stages, absorptive, adaptive and restorative along with ten indicators to measure capacities. Selected indicators were then checked for suitability for scope of the research based on opinions of seven experts. Subsequently, the critical infrastructure resilience index (CIRI) was introduced such that the numerical values for each indicator are aggregated using the Z score method. Statistical relations between the actual impact against disasters and CIRI calculated for administrative regions in Sri Lanka were used as the final step to validate the developed index.

Resilience index development is presented in this paper with a comprehensive methodology of developing and validation. Further, the case study results imply the weakness and strengths in each resilience capacities, which are important in decision-making.

Unavailability of disaster impact data and centralized data repository were main constrains in the validation process of this research. Hence proxy data was used to validate resilience index in this research.

This research identified and validated a novel approach of defining disaster resilience index for regional decision-making.

This study aims to investigate barriers and enablers of social media usage by zooming on one specific type of domain: disaster management. First, by systematically reviewing previous studies using a typology to social media usage, this study identifies the challenges often faced. Second, the results are visualized by qualitatively analyzing the focus group discussion data.

This paper opted for an inductive thematic approach of grounded theory, including focus group discussion with ten participants from diverse backgrounds working in the disaster domain. The data is transcribed verbatim and coded using Atlas.ti software.

The findings suggest that the vogue of social media significantly ascends its usage in disaster management. Regulatory, software, physical, authenticity, cultural and demographic rose as challenges for social media usage in disaster management. Findings further indicate enablers as the rise in mobile penetration, democratic participation, increase in living standards, two-way real-time communication, global reach, expeditious decision-making, no space-time constraint and cheaper source of information. Social media, compared to traditional media, is explored. This study has practical implications in helping authorities understand the barriers and enablers for social media usage in disaster management.

Qualitative data analysis of social media usage for disaster management has received scant attention. The main takeaway of this research is to offer clear findings of the purview of social media usage for disaster management. It demonstrates the challenges and enablers of disaster management using social media in the Indian context. Results indicate that leveraging social media for disaster management can extend decision-making for effective disaster management.

The Sendai framework for disaster risk reduction (DRR) 2015–2030 offers guidelines to reduce disaster losses and further delivers a wake-up call to be conscious of disasters. Its four priorities hinge on science, technology and innovations as critical elements necessary to support the understanding of disasters and the alternatives to countermeasures. However, the changing dynamics of current and new risks highlight the need for existing approaches to keep pace with these changes. This is further relevant as the timeline for the framework enters its mid-point since its inception. Hence, this study reflects on the aspirations of the Sendai framework for DRR through a review of activities conducted in the past years under science, technology and innovations.

Multidimensional secondary datasets are collected and reviewed to give a general insight into the DRR activities of governments and other related agencies over the past years with case examples. The results are then discussed in the context of new global risks and technological advancement.

It becomes evident that GIS and remote sensing embedded technologies are spearheading innovations for DRR across many countries. However, the severity of the Covid-19 pandemic has accelerated innovations that use artificial intelligence-based technologies in diverse ways and has thus become important to risk management. These notwithstanding, the incorporation of science, technology and innovations in DRR faces many challenges. To mitigate some of the challenges, the study proposes reforms to the scope and application of science and technology for DRR, as well as suggests a new framework for risk reduction that harnesses stakeholder collaborations and resource mobilizations.

The approach and proposals made in this study are made in reference to known workable processes and procedures with proven successes. However, contextual differences may affect the suggested approaches.

The study provides alternatives to risk reduction approaches that hinge on practically tested procedures that harness inclusivity attributes deemed significant to the Sendai framework for DRR 2015–2030.

Due to the unknown location, size and timing of disasters, the rapid response required by humanitarian operations (HO) faces high uncertainty and limited time to raise funds. These harsh realities make HO challenging. This study aims to systematically capture the complex dynamic relationships between operations in humanitarian settings.

To achieve this goal, the authors undertook a systematic review of the extant academic literature linking HO to system dynamics (SD) simulation.

The research reviews 88 papers to propose a taxonomy of different topics covered in the literature; a framework represented through a causal loop diagram (CLD) to summarise the taxonomy, offering a view of operational activities and their linkages before and after disasters; and a research agenda for future research avenues.

As the authors provide an adequate representation of reality, the findings can help decision makers understand the problems faced in HO and make more effective decisions.

While other reviews on the application of SD in HO have focused on specific subjects, the current research presents a broad view, summarising the main results of a comprehensive CLD.

This chapter analyzes the link between the digital divide, infrastructure regulation, and disaster planning and relief through a case study of the flood in San Jose, California triggered by the Anderson dam’s overtopping in February 2017 and an examination of communication failures during the 2018 wildfire in Paradise, California. This chapter theorizes that regulatory decisions construct social and disaster vulnerability. Rooted in the Whole Community approach to disaster planning and relief espoused by the United Nations and the Federal Emergency Management Agency, this chapter calls for leadership to end the digital divide. It highlights the imperative of understanding community information needs and argues for linking strategies to close the digital divide with infrastructure and emergency planning. As the Internet’s integration into society increases, the digital divide diminishes access to societal resources including disaster aid, and exacerbates wildfire, flood, pandemic, and other risks. To mitigate climate change, climate-induced disaster, protect access to social services and the economy, and safeguard democracy, it argues for digital inclusion strategies as a centerpiece of community-centered infrastructure regulation and disaster relief.

Conflict and violence are the main drivers of globally escalating humanitarian needs. Local grassroots initiatives are pivotal in distributing humanitarian supplies in the acute response phase until more established humanitarian aid organizations can enter. Nevertheless, scant research exists regarding the role of grassroots associations in providing humanitarian assistance during a military conflict. The purpose of this paper is to understand the role of grassroots associations and identify important themes for effective operations.

This paper adopts a case-study approach of three Ukrainian grassroots associations that began operating in the immediate days of the full-scale invasion of Ukraine. The findings are based on analyzing primary sources, including interviews with Ukrainian volunteers, and are supported by secondary sources.

Grassroots associations have local contacts and a contextual understanding of population needs and can respond more rapidly and effectively than large intergovernmental agencies. Four critical themes regarding the operations of grassroots associations emerged: information management, inventory management, coordination and performance measurement. Grassroots humanitarian response operations during conflict are challenged by personal security risks, the unpredictability of unsolicited supplies, emerging volunteer roles, dynamic transportation routes and shifting demands.

Grassroots responses are central to humanitarian responses during the acute phase of a military conflict. By examining the operations of grassroots associations in the early months of the 2022 war in Ukraine, the authors provide a unique perspective on humanitarian logistics. Nonetheless, more inclusive models of humanitarian responses are needed to harness the capacities and resilience of grassroots operations in practice.