Proceedings of the International ISCRAM Conference
Welcome to the official Proceedings of the ISCRAM Conference (2024-Present). ISCRAM stands for Information Systems for Crisis Response and Management, an acronym introduced at the first international ISCRAM Workshop which was held in Brussels in 2004. Established as an international non-profit in 2009, the ISCRAM Association promotes fundamental and applied research and the exchange of knowledge for the development and deployment of information systems for crisis management. ISCRAM researchers examine social and technical aspects related to the design, adoption, and use of information and communication technologies that support emergency and disaster mitigation, preparation, response, and recovery. Note: Proceeding from prior conferences (2004-2023) can be accessed at idl.iscram.org.
Information Systems for Crisis Response and Management
Evaluating Stress Manipulations in Scenario-Based Command and Control Training: A Multimodal Neural Network Approach
Command and Control (C2) in crisis response requires teams to coordinate, process information, make decisions, and take appropriate actions under stress. To effectively prepare C2 teams, scenario-based training is a core instructional approach to train and simulate these real-world pressures by manipulating stress. However, it remains unclear if participants actually experience and respond to these scenarios as intended and thus, whether the required capabilities and skills are effectively trained. In contrast to predominantly qualitative evaluations of scenario design, this study adopts a quantitative, multimodal approach using continuous measures of stress and coordination to evaluate and predict three escalating domestic violence arrest training scenarios: low-, medium-, and high-stress. Physiological stress was indexed via heart rate variability, and team coordination behaviors were systematically video-coded. We use recurrent neural network models to compare physiological-only, behavioral-only, and combined inputs to classify the scenario conditions, thereby advancing more evidence-based scenario design and evaluation of C2 training.
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Combining Event-Based and Functional Analysis in Municipal Risk Assessment: Capturing Cumulative Strain on Critical Societal Functions
Municipal risk assessment is typically structured around event-based analyses. While widely used, such approaches may limit the ability to identify how multiple simultaneous pressures collectively affect critical societal functions. This study examines how municipal risk assessment can be further developed by combining event-based and functional perspectives through an analytical hybrid approach within existing governance practice. Based on participatory action research in a Norwegian municipality, functional load assessment was integrated into an established scenario analysis framework. Rather than introducing new models, the approach extends existing analysis by enabling assessment of aggregated impact on each and across scenarios. The findings indicate a shift towards evaluating functional system performance, making cumulative strain across scenarios more visible. The study provides practice-based insight into how existing analytical frameworks can be extended without methodological reform, with implications for municipal risk governance and more resilience-oriented understandings of risk.
AI-Driven Cyberattack Risks - A Foresight Study
The digitalization has integrated artificial intelligence (AI) across multiple domains, including cybersecurity, creating both opportunities and risks. While AI enhances organizational defense through automated threat detection and incident response, it also enables scalable and autonomous cyberattacks by threat actors. This study examines future risks posed by AI-driven cyberattacks using foresight methodologies that combine horizon scanning and scenario development to identify current and emerging offensive AI capabilities. Previous studies indicates that present AI-enabled threats primarily involve social engineering and adversarial attacks, while future risks may include quantum-powered AI and fully autonomous systems. The study highlights a widening gap between offensive and defensive AI, driven by incentives, technical skill, and expertise. Three cyberattack scenarios involving AI tools were developed and validated through interviews with experts to assess plausibility and future relevance. Finally, the study proposes precautionary actions to strengthen organizational resilience, emphasizing anticipatory governance and cross-industry collaboration to improve threat intelligence accuracy
Co-Designing Team Design Patterns for Integration of New Technologies in USAR Operations
Emerging technologies such as drones, wearables, robots, and AI offer clear potential for improving safety and situational awareness in Urban Search and Rescue (USAR), but their integration into practice remains difficult in multi-stakeholder projects. This Work-in-Progress paper presents an iterative co-design approach, structured by Socio-Cognitive Engineering (SCE), to derive Use Cases, Team Design Patterns (TDPs) and Interaction Design Patterns (IDPs) for USAR operations. Over multiple stakeholder sessions during field-test events, interviews, focus groups, observations, and evolving design artefacts were combined to elicit operational needs, role divisions, information requirements, and autonomy boundaries. These inputs were translated into representative use cases and an initial set of USAR-specific TDPs and IDPs. The current results should be seen as a stakeholder-informed baseline rather than field-validated design knowledge, but they already offer reusable guidance for coordination, information routing, and human–technology collaboration in high-risk operations.
The Role of Simulation-Making in Crisis Unfolding
Believing precedes seeing. In this sense, strengthening alertness – a system’s ability to see problems before they impose themselves – requires emphasizing on what makes believing: mindfulness. Grounded in Karl E. Weick’s theoretical approach, this paper repositions crisis simulation as a sociotechnical process fostering organizational mindfulness and supporting the emergence of an organized collective mind under conditions of uncertainty. We argue that simulation design(ing), through scenario construction, entails a recursive reassessment of organizational preparedness mirroring the full (simulated) crisis lifecycle – before, during, and after. Drawing on a three‑year longitudinal ethnographic study of crisis simulation exercises, we analyze the intertwined processes of design, facilitation and feedback. Our findings show how simulations enact collective sensemaking and organizing through three interrelated reconfiguration dynamics: prospective, actualized and retrospective. By conceptualizing simulations as generative systems rather than mere training tools, the paper highlights their role in shaping organizational routines, coordination practices and reliability mechanisms in crisis contexts.
Understanding the influence of Weather on Traffic Incidents in Spain
Analyzing the impact of weather conditions on traffic incidents is relevant for assessing the vulnerability of road networks. While previous research has examined specific factors, a holistic framework regarding traffic incident protection remains necessary. This study applies an integrated multi-dimensional approach to evaluate precipitation effects in Gipuzkoa, Spain (2021–2024). Specifically, regression models are employed to quantify the relationship between precipitation and the occurrence, frequency, and severity of traffic incidents. Regression results quantify the observed patterns and indicate that precipitation conditions play a critical role in shaping road safety outcomes. Particularly, wet conditions increase the odds of incident occurrence by 70%, raise the number of incidents by 25%, and elevate the likelihood of severe outcomes by 24%. Crucially, when incidents coincide with registered flood events, the odds of a severe outcome more than double the normal scenario, identifying active disasters as a significant risk amplifier. Furthermore, the study shows that factors such as geography and the season amplify the overall risk chain of road incidents. These findings support the development of weather-responsive traffic management strategies.
Gaming a Crisis: Insights From a Bibliometric Literature Review
As crises become more prominent, serious gaming is emerging as an innovative tool to practice crisis management, yet a comprehensive overview of its applications remains lacking. Subsequently, this article poses the following research questions: What are the current applications of serious gaming in the field of crisis management? What are the emerging trends regarding the use of serious gaming in crisis management? Through a bibliometric review in combination with bibliographic coupling, we identified four themes: (1) healthcare, (2) disaster risk reduction and climate change, (3) adolescents and students, and (4) wargaming. Furthermore, our analysis reveals three critical trends: (1) diverse applications but inconsistent methodological rigor, (2) a divide between studying games as research objects (e.g., evaluating effectiveness) and using them as research tools (e.g., testing behaviors), and (3) a fragmented literature, hindering unified theoretical or practical frameworks.
Extracting, Locating and Visualizing Geospatial Rescue Information in German-language Social Media
Timely extraction of rescue-related data from social media is vital for emergency response, with event extraction and geolocation playing a key role. This paper presents a demo system that leverages Large Language Models (LLMs) and Knowledge Graphs (KGs) to identify rescue-related data from social media streams and integrate this information into a continuously updated KG, with a focus on the German city of Hamburg. Our approach utilizes an LLM to process unstructured social media text, accurately identifying events and relevant location references. LLMs in combination with in-context learning are applied for event extraction as well as geoparsing. The extracted and linked information is stored in a KG, which is both queryable for further analysis and supports downstream applications such as interactive map-based visualizations, providing real-time awareness for emergency services. Specifically, our geoparsing methods bridge the gap in the German setting, achieving state-of-the-art performance on the benchmark dataset MobIE.
Are We Ready for the Next Attack? Empirical Findings on Cybersecurity Awareness and Training
This paper examines how cybersecurity training and awareness building contribute to organizational preparedness against cyberattacks, particularly in organizations where cyber-physical and supply-chain interdependencies amplify cascading impacts. Building on the evolving threat landscape reported by ENISA and CrowdStrike and policy drivers such as the EU NIS2 Directive, we report findings from a representative empirical study of $n = 1014$ employees in Austrian companies. The results show that basic technical safeguards (e.g., antivirus, firewalls, access control) are widely implemented, while organizational preparedness remains uneven as emergency plans and clear incident procedures are often missing or unknown to staff. Cyber threat knowledge is largely limited to common terms (e.g., phishing), and awareness of supply-chain cybersecurity is particularly underdeveloped. Training is not systematic, since only about half participated in trainings over the last 12 months, and content retention declines sharply if it has been over one year since the training took place. We discuss implications for preparedness and recommend recurring, practical training cycles, including tabletop and simulation-based exercises that make incident response and supply-chain dependencies actionable for employees.
Close Calls in Cyberspace: Strengthening Cybersecurity by Learning from Near‑Misses
Organizations can improve their cybersecurity by learning from both incidents and success stories. These successes can, in many cases, be defined as ‘near-misses’, when a potentially successful attack is stopped just in time. However, it is unclear what can be categorized as a near-miss in cybersecurity. To bridge this gap, we define four distinct categories of near-misses in cybersecurity, along two axes of interest, being the locus of intervention (human or system) and the nature of the intervention (active or passive). We argue that understanding the workings of these near-misses can support organizations in improving their cybersecurity practices to build stronger futures. To do so, we outline a research agenda of questions and topics that require further examination to fully leverage the insights on near-misses in organizational cybersecurity.
Popularity Without Legitimacy? Comparing Trust in Television Meteorologists and YouTube Weatherfluencers
During severe weather events, people must interpret rapidly evolving information to make time-sensitive safety decisions. Broadcast meteorologists have traditionally served as credentialed intermediaries within established media organizations, while independent "weatherfluencers" on YouTube have emerged as prominent real-time interpreters for large and growing audiences. This mixed-methods study (survey n=58; interviews n=8) provides one of the first empirical comparisons of how viewers evaluate broadcast meteorologists against YouTube weatherfluencers across credibility, legitimacy, objectivity, and practical utility. Broadcast meteorologists were consistently rated higher on credibility, legitimacy, and safety utility, while weatherfluencers achieved parity on objectivity. Yet weatherfluencer audiences continue to grow, revealing a critical decoupling between audience attention and official or professional authorization that existing crisis communication models do not fully account for. Qualitative findings illuminate the mechanisms underlying these judgments and their implications for emergency communication in hybrid information ecosystems.
Extracting the 'Why': Flood Cause Annotation and LLM Benchmarking from Multi-Platform Crisis Data
We present a framework for extracting and classifying flood cause information from crisis-related online media data. By harnessing data across multiple online platforms from both social media and online news sources, we can understand causes of floods ranging from environment factors like heavy rainfall to human-related factors like infrastructure failure. This task is operationally critical not only for disaster response but also for public accountability, yet it remains a challenging task for automated systems. Our key contributions are: (1) a comprehensive flood cause annotation protocol that operates at two levels of granularity i.e., document-level and sentence-level, with strict exclusion criteria for distinguishing causal statements from impacts; and (2) initial benchmarking experiments to evaluate the performance of zero-shot large language models (LLMs) on the classification task. Although the underlying corpus—covering 70 distinct flood events from 2022–2025 across News, X (formerly Twitter), YouTube, and Bluesky is not publicly released, we describe the data construction methodology in full to support replication on comparable data. Our initial analysis on a stratified 10-event sample demonstrate that LLMs struggle: they have high recall but suffer from low precision in flood cause classification.
LLM-Powered Automatic Translation and Urgency in Crisis Scenarios
Large language models (LLMs) are increasingly proposed for crisis preparedness and response, particularly for multilingual communication. However, their suitability for high-stakes crisis contexts remains insufficiently evaluated. This work examines the performance of state-of-the-art LLMs and machine translation systems in crisis-domain translation, with a focus on preserving urgency, a critical property for effective crisis communication and triage. Using multilingual crisis data (TICO-19, 30 languages) and a newly introduced urgency-annotated dataset of 100 scenarios translated into 29 languages, we show that dedicated translation models and LLMs exhibit substantial quality degradation, particularly for low-resource languages. Beyond translation quality, we conduct a human annotation study revealing a striking asymmetry: human assessors maintain consistent urgency judgments regardless of prompt language, while LLM-based urgency classifications vary widely across languages for identical scenarios, at times spanning the full range from Not Urgent to Critical. These findings highlight significant risks in deploying general-purpose language technologies for crisis triage and underscore the need for multilingual, human-centered evaluation frameworks.
When Social Media Images Need Words: Measuring Context Gap and Fusion Tax in Crisis Image Captioning
Crisis images on social media can be difficult to interpret at scale, and many carry meaning embedded in text or symbols (e.g., radar screenshots, evacuation notices). This limits vision-only captioning for situational awareness. We quantify a central trade-off in multimodal captioning: adding post text can reduce omission-driven ambiguity (the Context Gap), but it can also introduce text-driven errors (the Fusion Tax). Using 204 high-priority image–post pairs from CrisisFACTS, we compare Vision-only and Vision + Text captioning across Gemini 2.0 Flash, Qwen2.5-VL, and BLIP. We find that post text improves accuracy for Gemini and Qwen largely by reducing misidentification and scene-type errors, while sometimes amplifying hallucinated (unsupported) details. BLIP, however, does not reliably fuse modalities in our setup. When post text is provided, it often collapses into simple text echoing rather than producing image-grounded captions. We discuss implications for multimodal fusion in crisis informatics and outline next steps for image-type evaluation and routing.
Where Is Cybersecurity? An Analysis of U.S. State Hazard Mitigation and Emergency Plans
Despite the growing convergence of physical and cyber risks, U.S. state disaster planning remains uneven in its treatment of cybersecurity threats. While federal guidance promotes an all-hazards approach to preparedness, the extent to which state-level hazard mitigation and emergency plans address cyber risks has not been systematically characterized. We present a national assessment of cybersecurity content in State Hazard Mitigation Plans (SHMPs: all 50 states) and State Emergency Plans (SEPs: 45 states). Using content analysis, we examine cyber risk framing, coverage across the emergency management cycle, and governance mechanisms (responsibilities, standards, detection, reporting). Although many plans reference cybersecurity, coverage varies widely and is often limited for recovery/continuity, threat detection, and procedural reporting guidance. SHMPs emphasize risk identification and mitigation, while SEPs emphasize agency roles and response coordination. We discuss implications for cyber crisis governance and identify practical priorities for strengthening state planning and supporting more coordinated cyber disaster readiness.
EQUAL: Entity-Enhanced QUery Expansion for EquitAble Crisis Summarization via KnowLedge Graphs
Disaster response efforts face persistent challenges in ensuring equitable aid and information access for all affected populations. Marginalized communities, including the elderly, persons with disabilities, people experiencing homelessness, low-income households, non-English speakers, and geographically isolated residents, face heightened risk during disasters and are more likely to experience delays in receiving aid, evacuation support, and critical information (Wilson et al. 2021). We present EQUAL (entity-enhanced query expansion for equitable crisis summarization via knowledge graphs), a work-in-progress framework that combines dual-model entity-enhanced query expansion with an equity-aware GraphRAG (Graph-based Retrieval-Augmented Generation) pipeline. EQUAL constructs crisis knowledge graphs enriched with vulnerability–resource connections and generates summaries through community-level synthesis. Evaluated on 18 real-world disaster events from the TREC CrisisFACTS dataset, EQUAL outperforms all baselines on equity-focused metrics—vulnerable group coverage, intersectional coverage, and statistical parity—and shows marked gains in explicit mentions of vulnerable populations, geographic specificity, and actionable resource information. It also remains competitive on standard semantic quality metrics.
Sociodemographic characteristics in social media
Socioeconomic Drivers of Post-Rainstorm Community Resilience: A Comparative Analysis of Two Cases in North China
Short-duration and high-intensity extreme rainfall events increasingly disrupt urban systems. Under escalating climate risks, understanding post-disaster recovery dynamics has become critical for resilience assessment and recovery planning. However, research on post-rainstorm recovery remains constrained by limited data availability, insufficient temporal resolution, and inadequate identification of spatiotemporal heterogeneity in recovery trajectories. To address these limitations, this study uses daily NASA VIIRS nighttime light (NTL) time-series data as a proxy for observable functional recovery in two flood-affected areas in North China following the July–August 2023 extreme rainfall event. A pixel-level Recovery Rate Index is constructed to quantify rebound dynamics, and time-series clustering is applied to identify distinct recovery trajectories. Explanatory modeling is further conducted to examine the socioeconomic and environmental drivers associated with different recovery modes. Results show that recovery regimes differ substantially across terrain contexts. The comparison further suggests that mountainous and plain urban environments are characterized by different dominant recovery mechanisms, highlighting the need for geographically differentiated post-disaster recovery assessment and resilience planning.
Extreme rainfall, nighttime lights, disaster recovery
Dynamic 3D Fire Spread Prediction via Visual Mapping: A Hybrid GNN and Cellular Automata Approach
Wildfire damages in the United States exceeded $147 billion between 1980 and 2024, yet a critical gap exists between macro-scale fire spread prediction and micro-level visual detection. This mismatch is especially pronounced in Wildland-Urban Interface (WUI) environments, where fire propagates through fine-grained, structure-scale interactions. We present a micro-scale fire spread prediction methodology using Graph Neural Cellular Automata (GNCA), combining 3D voxel-based fire representation from multi-view video with a data-driven cellular automata framework that learns propagation rules directly from observations. To validate our approach, we conducted controlled burn experiments in Georgia with multi-angle video capture, binary fire segmentation labels, time-to-arrival annotations, and environmental measurements. Key contributions of this work-in-progress research include a WUI-representative benchmark dataset, a visual hull-based 3D voxel construction framework, and a GNCA predictive model for next-frame fire ignition at meter-scale resolution. This work establishes a foundation for fine-grained fire spread tools supporting tactical firefighting decisions.
Calling Out a Cyber Crisis: A Typology for Cyber Incident and Crisis Management
Only a subset of cyber incidents in organizations escalates into a full-blown cyber crisis that overwhelms technical response capacities and has severe societal consequences. We clarify the transition from incident to crisis management and conceptualize escalation as a sociotechnical process shaped by sensemaking, decision-making, and multi-actor coordination. To do so, we synthesize cyber incident and cyber crisis management literature and propose a typology to illustrate the pressures and turning points in this transition based on actual cases. We discuss implications for preparedness, incident management, sensemaking, and post-incident learning that integrates technical remediation with crisis leadership and communication.
Situational Disabilities and Shared SA devices: Insights from Full-Scale Emergency Exercises
This study examines how situational disabilities (SDs) act as barriers to situational awareness (SA) in multi‑organisational emergency response, using empirical data from four full‑scale crisis exercises (2022–2025) in Norway. Drawing on Endsley’s SA framework and models of disability (medical/social/ICF), we combine observations, qualitative interviews and open-ended survey responses to explore how contextual factors, device design, and human stress interact to produce Situational Disabilities (SD) that degrade perception, comprehension, and projection. Findings show pronounced sensory SDs (auditory masking and reduced verbal communication), device‑induced SDs (auto‑muting of radio channels, small screens, fine‑motor requirements incompatible with gloves), and cognitive SDs (information overload, attentional tunnelling, SA-level lock‑in). SDs often amplify Endsley’s SA “demons” by creating emergent socio‑technical accessibility barriers: protective equipment, noise, extreme weather, and interface affordances frequently prevent actors from receiving or acting on critical shared information. We argue that crisis systems and Shared SA devices must be designed with situational‑disability awareness, adopting universal‑design principles, configurable audio/visual channels, and resilient interaction modalities, to strengthen shared situational awareness under real‑world stressors.
Layered Automation in Hospital Crisis Readiness: A Scoping Review and Framework for Deterministic, Intelligent, and Agentic Systems
Hospitals face recurring crisis conditions, including pandemics, mass casualty events, natural hazards, and infrastructure disruptions, which stress operational coordination, compress decision cycles, and amplify information fragmentation. Automation has increasingly been used to support crisis responses through workflow execution, decision support, and predictive modelling. However, crisis automation literature remains fragmented across system types, hazards, and organizational contexts, and lacks a governance-aware synthesis that distinguishes between levels of autonomy and corresponding accountability requirements. This study reports an scoping review using EBSCOhost databases (PRISMA-ScR) of hospital crisis automation. Across CINAHL Ultimate and MEDLINE Ultimate, two search families (core automation; AI/agent terminology) yielded 2,297 records. A supplementary AI-assisted discovery search yielded 1,000 additional records for review. After screening predefined inclusion criteria and deduplication, 116 studies were included in the analysis. The synthesis identifies two dominant strata of crisis automation: deterministic workflow automation and bounded intelligent decision augmentation. In contrast, policy-constrained agentic automation, defined as goal-directed orchestration across heterogeneous systems, appears largely absent as a mature operational form in literature. Building on this synthesis, we propose a layered framework that links automation strata to integration patterns, failure modes, and escalating governance controls. A concrete use case illustrates how deterministic, intelligent, and agentic layers would differ in a surge scenario, highlighting practical constraints regarding data access, interoperability, and human authorization. This study contributes (i) a consolidated map of hospital crisis automation research, (ii) a governance-aware layered framework for disaster public health and healthcare informatics, and (iii) implications for designing and evaluating emerging agencies in crisis settings.
Security and Privacy in Smart Home Systems During Crises and Emergencies: A Systematic Literature Review
This systematic literature review (SLR) investigates security and privacy threats, and solutions for Smart Home Systems (SHS) in crises contexts. SHSs are interconnected devices of which a breach of any one component can compromise the entire system. This SLR answers two questions: (RQ1) What are the documented security vulnerabilities and privacy risks associated with smart homes in emergency scenarios like fires or burglaries? and (RQ2) What technical or architectural solutions have been used to enhance the resilience of smart homes against security vulnerabilities and privacy risks? The analysis reports data management risks; (83.33% of articles) are most prevalent threats. Human related factors are less dominant but can jeopardize SHSs. Cryptography is mostly adopted in implementing security and privacy solutions while less studies focus on AI/ML and human practices. This research highlights the gap in crises-focused analysis, as existing studies dwell on general cyber security with no clear-cut solutions for crises
Dynamic Allocation of Mobile Units in Disaster Support: A Case Study of the Noto Earthquake
This study analyzes how mobile units were used during the 2024 Noto Peninsula Earthquake and clarifies how needs changed over time and what information management issues arose. Field investigations in Suzu City and Shika Town show that demand for mobile toilets, laundry units, and residential trailers shifted as the recovery process progressed. In several cases, mismatches between unit specifications and evacuee composition reduced effectiveness. Existing disaster information systems are largely sector-based and designed around fixed facilities, making it difficult to coordinate mobile resources in response to changing conditions. To address this gap, this study proposes a two-layer information management model that links individual-level data with mobile unit data through aggregated indicators. The proposed Dynamic Unit Allocation Framework provides a foundation for managing mobile disaster support more effectively.
Citizen Observatories for Flood Risk Governance: A Whole of Society Approach
Flooding is a major natural hazard that is becoming more frequent and intense due to climate change and urbanization. Traditional flood management systems often lack sufficient citizen involvement and weak incorporation of local knowledge into risk assessment and response processes. Citizen observatories (COs) have emerged as a transformative solution, incorporating community-based monitoring and crowdsourced data into environmental governance. This work-in-progress paper aims to examine the role of CO in environmental and flood risk governance through a combined scientometric and qualitative analytical approach. The study systematically investigates international policy frameworks that embed participatory governance, maps global CO–integrated flood risk management initiatives, and evaluates the design of European Union CO programmes under FP7, Horizon 2020, and Horizon Europe, with a view toward their contextual adaptation for Nova Scotia, Canada.
Multi-Threading and GPU Acceleration: The Case for Faster Simulators for Maritime Search and Rescue Operations Planning
In decision support systems (DSSs) for maritime search and rescue (MSAR), simulation can be used to calculate the probability of success of a search operation tasking aircraft and vessels to search patterns. Nonetheless, single-threaded simulators remain a bottleneck in DSSs for MSAR, where thousands of candidate search operations generated by an optimizer have to be evaluated within a few minutes. Slow simulations also influence the design of DSSs when slight manual corrections to an operation require waiting for reevaluation. In response to the need for faster simulators, we redesigned the simulation pipeline and developed two parallel simulators: one based on CPU multi-threading and one based on GPU acceleration. Experiments show that CPU multi-threading provides a 6.7-fold acceleration over the single-threaded baseline, whereas the GPU acceleration yields a 260-fold speedup. Such speedups can render DSSs more responsive and enable optimizers to provide better recommendations, ultimately leading to more effective MSAR operations.
Rapid Identification of Moving Contaminant Sources Through Physics-Based Modelling
In an act of sabotage or terrorism, hazardous material might be released deliberately into the atmosphere to threaten individuals, e.g., those operating critical infrastructure. Hazardous materials in such a scenario include toxic industrial chemicals (TICs), which are often invisible to the human eye, making it difficult to detect and respond to releases in a timely manner. This contribution considers the scenario of an airborne hazardous release requiring rapid and reliable assessment, with a chemical, biological, radiological, and nuclear (CBRN) sensor system providing scarce and local measurements. We present a novel algorithm that couples these data with an advection–diffusion model to detect, localize, and quantify a moving and time-varying contaminant source. Unlike many existing methods, the approach identifies sources with unknown occurrence time and trajectory by incorporating spatial sparsity as prior information. The feasibility of the approach is demonstrated in a two-dimensional computational domain. To further increase the technology readiness level, we additionally propose a calibration methodology for the required three-dimensional flow models based on wind tunnel experiments. Finally, a strategy for coupling the framework with real-time sensor data within a digital twin environment is outlined to enable predictive decision support in emergency scenarios.
Comparative Analysis of Learning Conditions across Different Roles in a Large National Crisis Management Exercise
Large-scale crisis exercises are commonly treated as shared learning events, yet participation within such exercises is often differentiated across organisations. This Work in Progress examines how differentiated participation in a national crisis exercise series shapes organisational learning conditions and coordination demands. Drawing on empirical material from the Swedish KBÖ24 exercise series, the study compares organisations participating partly and fully in the exercise series as different activity positions within the same exercise system. Using Activity Theory as a sensitising analytical lens, the analysis focuses on differences in exposure to decision-making under uncertainty, operational roles, participation in evaluation processes, and learning trajectories over time. The results suggest that large-scale exercises function as differentiated learning environments, where exercise design and role allocation shape the visibility of system-level coordination challenges and opportunities for reflective learning. Complementary sensemaking perspectives help explain why learning outcomes are position-dependent rather than performance-dependent. The paper contributes to early analytical insights into how exercise design influences learning across complex, multi-organisational crisis management systems.
SenseGate: Distributed and Reliable Monitoring for Flood-Protected Areas
This paper reports about ongoing research for protecting critical infrastructure against floods. Aligned to a real use case in the Port of Hamburg, we present an approach for reliably monitoring floodgates. We analyze existing processes for safe floodgate operation and devise the design of SenseGate, a distributed monitoring system of increased resilience. We describe how the system integrates both autonomous sensors and technicians to digitize the polder status and to trace critical operations. Communication incorporates authenticated messaging and redundant communication links to improve the robustness of the system. Measurement challenges of reed switches and inductive sensors are discussed in the context of balancing energy consumption and accuracy. We propose designs for an energy neutral sensor, a companion device for field technicians, and a dashboard which reports cross-validated information to the overseeing operator. Finally, we systematically analyze how failure modes affect the SenseGate system and how impairments can be prevented.
PIO Crisis Communication on Social Media Across Agencies: Social Listening, Public Perception, and Design Considerations
Public information officers (PIOs) increasingly rely on social media for crisis communication, yet sustained social listening, especially for tracking public perception, is difficult to maintain under time, staffing, and platform constraints. We present an exploratory qualitative interview study of seven PIOs from government agencies in the U.S. Mountain West. Participants described different objectives on blue-sky days versus during emergencies: on blue-sky days, social media was used mainly for preparedness outreach and relationship-building, while during emergencies it was used to share incident updates, amplify partner agencies, and reassure affected communities. Participants also described social listening as important but unevenly sustained in practice. While many participants valued monitoring public questions, reactions to official messages or misinformation as important, most still relied on manual checks or engagement on their own accounts. Differences in PIO responsibilities, staffing, and agency role shaped whether social media work remained primarily one-way or included more sustained listening. Based on these findings, we derive design considerations for scheduling and pre-planning, monitoring, post review, and multi-agency coordination. Participants were generally more comfortable with bounded automation for monitoring, review, and other back-end support than with automation that produces public-facing messages, aside from limited and clearly constrained uses such as routine after-hours acknowledgments.
Virtual Reality Applications for Civilian Emergency Preparedness Training: A Bibliometric Review
This bibliometric review examines serious games (SG) supported by virtual reality (VR) technologies for civilian emergency preparedness training. It maps publication trends, research themes, and evaluation practices in 66 studies published from 2010–2024 using a PRISMA-ScR approach. The quantitative analysis was combined with qualitative coding to assess how the papers defined evaluation methodologies and assessed their effectiveness for VRSG. The results point to notably increased research activity after 2018. Fire and evacuation scenarios dominate, while earthquakes, floods/tsunamis/typhoons, and hazards such as landslides or extreme weather appear far less often. Most studies rely on in‑game behaviour tracking and self‑report questionnaires, with fewer controlled comparisons and very rare long‑term retention assessments. The field is expanding and technically varied, but remains fragmented in outcome measures, reporting standards, and evidence linking in‑game performance to real‑world preparedness. Common methodological limitations include small samples, short‑term evaluations, and limited validation of learning transfer.