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
Transdisciplinary Research Strategy On Crisis Management And Resilience Of Population
Indonesia and Vietnam are countries subject to a wide range of risks. The Archipelago already has a culture of preparedness and response, through its National Agency for Disaster Management (Badan Nasional Penanggulangan Bencana, BNPB). In Vietnam, the permanent structure is the Vietnam Disaster Management Authority (VNDMA), which reports to the Ministry of Agriculture and Rural Development (MARD). France is currently providing both countries with its knowledge of hazards (seismic, volcanic, but also climate change and forest fires) through its operators and research centers as well as the Directorate for Security and Defense Cooperation, in a dispersed manner. At the time the project was submitted, BNPB wanted to strengthen its “actionable” knowledge base through a collaboration with France and around a vision combining governance and resilience in a growing tourism destination. From their side, the French embassies in Indonesia and Vietnam were both concerned about the risks to which tourists are exposed in these countries. The “Transdisciplinary research strategy on crisis management and resilience of population” ‘STRAP’ project has been funded as part of the Fonds Equipe France (FEF). It aims to bring together teaching, research and civil protection teams to improve the safety and resilience of populations about the issue of risk prevention and crisis management. It has two main objectives: The structuring of a 5-year roadmap, aimed at BNPB, for the implementation of transdisciplinary Franco-Indonesian research (researchers/ practitioners) and including a Vietnamese and/or ASEAN dimension; The implementation of two transdisciplinary pilot projects on “resilience of population and digital technology” in the rapidly developing tourist areas of Labuan Bajo in Indonesia and the Province of Quanh Binh in Vietnam. To do so, a community-centered approach has been adopted in the implementation of both objectives. This book presents and gathers the roadmap and its constitutive topics. It has been collaboratively realized from July 2024 to April 2025: From July to October 2024, Indonesian, Vietnamese and French research teams have been reached to contribute to this roadmap and characterize the existing and future areas of collaboration. Once the project and its main stakes were presented, each team has been asked to propose a topic or challenge. Based on this first pool of research teams, a first hybrid workshop took place in December 2024, where each team presented their area of expertise and the challenge(s) they were willing to address in the road map. Based on this first workshop, teams representing at least two countries have been proposed to address a specific challenge and foster their cooperation. Each teams met at least once online in order to discuss and prepare the content of the constitutive challenge(s) of the roadmap presented during a second workshop in March 2025. This second workshop gave a first overview of the roadmap, completed by two additional on-going Franco-Indonesian projects in Lampung and Semarang. Feedbacks have been formulated, specifically by BNPB, in order to support the teams to finalize the roadmap. Also, a call for independent contributions from research teams and/or from the Asia Pacific area have been opened through the ISCRAM community network to complement the roadmap. The final hybrid workshop of presentation of the roadmap by team took place the 24th of April 2025. It included the contextualization of the project and its transdisciplinary stakes (see the following Introduction by Udrech), the results of the collaboration between Indonesian, Vietnamese and French teams (see Part 1), the independent proposals as well as the first insights of the both pilot-surveys conducted in the Province of Quang Binh (Vietnam) and in Labuan Bajo (Indonesia) (see Part 2).
Copyright (c) 2025 Transdisciplinary Research Strategy On Crisis Management And Resilience Of Population (STRAP Project)
System Development and Evaluation for Mass Casualty Incidents Triage with Virtual Realityand Artificial Intelligence
This study investigates the integration of Virtual Reality (VR) and Artificial Intelligence (AI) to enhance pre-hospital triage training for Mass Casualty Incidents (MCIs). Traditional training methods, such as field drills and full-scale simulations, are often costly and logistically challenging, while simpler methods like tabletop exercises remain limited in realism. To address these limitations, a VR learning tool was developed to simulate realistic emergency scenarios, providing emergency healthcare professionals with an immersive and cost-effective training environment to refine triage skills. The VR learning tool records both VR sensor data and speech data, and then utilizes statistical and AI methods (such as automatic speech recognition, and natural language processing) to process these data for evaluation. The survey results showed that participants with varying levels of experience found the VR training highly immersive and engaging. Additionally, AI-driven analysis of speech data from the training demonstrated improved consistency and correctness in participants’ communication over time. This research demonstrates VR’s potential as a valuable supplement to traditional training, identifying key areas for future development.
Copyright (c) 2025 Proceedings of the International ISCRAM Conference
AI Enabled Healthcare Services During Cross-Border Medical Emergency and Regular Patient Services (ESCORT project): System architecture and first results in Emergency Medicine Scenario
The ESCORT project (ESCORT Project, 2024) aims at connecting up-to-date IoT Wearables, Artificial Intelligence and Machine Learning towards building a framework for a better cross border response for medical emergencies and public health protection and resilience. The ESCORT project brings together healthcare providers from five European member states and one associated member Israel specialized in emergency medicine and offering regular health and care services. New and better integrated digital services have been designed in consultation with stakeholders including patients, patient advocacy groups, health and care service professionals and providers. This paper presents a detailed outline of the project goals and ambitions supplemented by the requirements considered in the development of the overall platform architecture. Out of the 10 tools identified for integration in the project, two will be further elaborated with initial requirements that have been collected, and a first analysis of the solutions will be presented.
European Health Emergency Preparedness and Response Authority (HERA)
Assessing Drone Performance and Probability of Detection for Search and Rescue Operation in Northern Canada
Drones are increasingly valuable in Search and Rescue (SAR) operations, offering rapid aerial coverage and improved situational awareness in remote environments. However, environmental constraints and operational limitations affect their effectiveness. This study evaluates drone deployability and operation in SAR missions across Northern Canada through statistical analysis of SAR operability in various weather conditions, accounting for seasonal variations, and Probability of Detection (PoD) in different search scenarios. The first research question classifies operability categories based on historical weather data, identifying periods and locations for reliable drone deployment, considering factors like temperature, wind speed, and seasonal changes. The second research question estimates PoD, assessing the likelihood of detecting Persons in Distress (PiDs) with drones under varying conditions, including terrain, sensor capabilities, and target visibility. This research aims to evaluate drone usefulness in SAR operations by identifying constraints, informing deployment planning, and supporting technological advancements to improve search effectiveness in remote, high-risk areas.
Data Ecosystems Support for Disaster Risk Reduction (DRR) and Resilience at Borders
Data ecosystems represent a promising avenue to support Disaster Risk Reduction (DRR) and resilience. Especially in fragmented settings such as cross-border regions, data ecosystems can foster data sharing and regulation among DRR actors. However, recent research has suggested that the creation and maintenance of data ecosystems is challenging. Overall, DRR actors still lack a comprehensive overview of the nature of the impact that a data ecosystem can have on its members. Despite the scarcity of cross-border data ecosystems for DRR, a comprehensive examination of their impact is necessary to support future investments. This research addresses this lack by thoroughly examining the case of the Italian French border. Through a qualitative research design, our research team has been collecting and analyzing data from observations, interviews and focus groups. Drawing on the Theory of Change (ToC), we propose a comprehensive model of the support from a cross-border data ecosystem for its members’ resilience. This research contributes to research on data ecosystem in the field of disaster management by highlighting the importance of nurturing data documentation, collaboration and trustful ties within data ecosystems.
Integrating Epidemiological Modelsand Hospital Resource Management – A Demonstration of the Agent-Based Simulation Tool HosNetSim
This paper presents the design and implementation of a unidirectional data interface connecting epidemiological forecasting models with hospital resource management tools, developed within the PROGNOSIS project to enhance hospital preparedness for pandemics and epidemics. We demonstrate the practical application of this interface through HosNetSim, a novel agent-based simulation tool specifically designed to support hospital and hospital group administrators, as well as public health authorities in hospital supply chain decision-making. The interface provides standardized, geographically specific, and daily updated forecasts of hospital burden across multiple respiratory diseases and care levels, enabling what-if scenario analysis. HosNetSim utilizes these forecasts to evaluate and visualize critical supply chain decisions, including inventory management policies, inventory pooling, and transshipments. By simulating realistic operational scenarios, HosNetSim illustrates the trade-offs between service levels and associated costs from the perspectives of individual hospitals, hospital groups, and regional networks.
Strengths and Limitations of Existing STAMP Tools for Analyzing System of Systems Risks
The System Theoretic Accident Model and Processes (STAMP) considers safety as a dynamic problem by focusing on interactions among system components rather than on component failure. However, analyzing complex system risks with STAMP can be challenging due to manual processes. Therefore, software tools supporting STAMP analysis have been developed. An evaluation of five tools (ASTAH, STPA, Capella, XSTAMPP, STPA Master Lite, and Visio Pro) was conducted, focusing on systems of systems (SoS) risk analysis. While all tools demonstrated compliance with the STAMP risk analysis procedure, with some tools offering additional features, a shortcoming in scalability limits the support for risk analysis of SoS. This makes risk analysis with the available STAMP tools challenging for SoS. Recommended enhancements include mechanisms to address SoS characteristics, scalability, collaboration, and improved usability. The study contributes to research on SoS risk analysis and provides direction for advancement in risk analysis tool support.
Monitoring & Responding to Information Ecosystem Incidents: A Conceptual Framework Understanding Information Ecosystem Risk and Leveraging Academic Expertise for Information Incident Response
Our digital information ecosystem shapes the flow of information, the spread of disinformation, the power of adversarial actors, truth, trust, and democracy itself. Characteristics of this ecosystem impact crisis prevention, mitigation, preparedness and response; yet, research on the intersection between digital information ecosystems and crisis and emergency management is scarce. The capacity to deliver and respond to incidents in the information ecosystem is crucial in combating emerging digital threats, such as foreign influence, misuse of generative AI, and online extremism. This paper addresses these challenges by defining the digital information ecosystem and information incidents as they relate to crisis and emergency management and provides an interdisciplinary information incident response framework that integrates conceptual academic research with practitioner perspectives for improved crisis response.
Exploring the Concept of a Digital Twin Smart City for Disaster Emergency Preparedness Planning in Nova Scotia, Canada
Cities worldwide are grappling with complex challenges, including rapid urbanization, crowd control, ageing infrastructure, and the necessity for efficient emergency response. These issues are exacerbated by climate change and the increasing risk of natural disasters, compelling innovative solutions for sustainable urban development. The emergence of the Smart City concept has provided a transformative approach to addressing these challenges through advanced technologies to enhance quality of life, urban resilience, and sustainability. Digital Twins (DTs) have emerged as a game-changing tool among these technologies. A DT is a virtual representation of a physical city that mirrors its systems, processes, and operations in real-time. This paper conceptualizes DTs for disaster-resilient smart cities, including descriptive, diagnostic, predictive, prescriptive, and autonomous according to their maturity level. This exploratory paper contributes to the emergency management literature by enhancing the understanding of how different DT maturity levels can be deployed in disaster contexts, especially for flood hazards in Nova Scotia
Improving Coastal Adaptation: Climate Change Impact Modelling for Resilient Communities
Continuous change in climate highlights the vulnerability of coastal communities, there will be a lot of worries among the coastal communities with the risk of rising sea levels, storm surges, and extreme weather conditions, The factors that affect flooding in low-lying coastal regions are urban expansion, deforestation, and climate-driven changes. Machine Learning and geospatial tools are used to create accurate flood susceptibility maps and help in identifying high-risk zones and mitigating disasters. This study helps in identifying the risk of flood using two machine learning algorithms Random Forest and Support Vector Machine for predicting flood-prone areas. The experiment was performed on the Permanent Service for Mean Sea Level (PSMSL) dataset. The random forest algorithm accuracy of calculating Flood situation is 78%, and that of the Support Vector Machine it is 71%, also random forest shows high precision around 74% which is greater than the Support vector machine which is 71%, the comparison of recall is about 86% for random forest and that for Support vector machine it is 71%. The ability of machine learning to accurately identify high-risk flood zones and provide reliable predictions for disaster preparedness is demonstrated by these results. In addition, this study investigates ML-driven methods for conserving cultural heritage and enhancing community-based climate adaptation. The study concludes by emphasizing the crucial role of ML-driven predictive models in climate resilience planning, particularly in improving flood risk assessments and disaster mitigation, and suggests integrating real-time big data analytics, cloud computing, and nature-based solutions for stronger coastal protection and sustainable adaptation
SafeSigns: Enabling Community Resilience Communication for the Deaf and Hard of Hearing
Emergency Management (EM) strategies often overlook the communication challenges faced by the Deaf and Hard of Hearing (DHH) community, limiting their involvement in disaster preparedness and response. This paper introduces SafeSigns, a geospatially enabled toolkit designed to bridge this gap by facilitating hazard reporting and communication by and for DHH individuals. By integrating Geographic Information Systems (GIS) with user-centered design, SafeSigns empowers users to report incidents, identify hazards, and coordinate with Public Safety (PS) officials. Unlike traditional EM technologies, which rarely prioritize accessibility, SafeSigns leverages ArcGIS Pro, React Vite, and TypeScript to ensure usability, efficiency, and accessibility. This research represents one of the first ISCRAM-related efforts to explicitly include DHH communities in EM. Findings support a more inclusive and participatory approach, demonstrating the significance of geospatial solutions in enhancing community resilience. Future work will refine SafeSigns through real-world testing and explore applicability to other vulnerable populations in disaster response.
Enhancing Norway’s Local Emergency Preparedness with Municipal Crisis Team Exercising
This case study describes and discusses the outcomes of an emergency preparedness exercise designed to enhance team processes in a municipal crisis response team. The study presents the case of an emergency preparedness exercise simulating a cruise ship in distress in the High North. The exercise was facilitated by a cross-disciplinary team of faculty members and subject matter experts from emergency preparedness organizations. The results highlight the student assessment of the exercise and identify critical exercise design factors associated with enhanced learning, such as scenario realism, timely preparation, and regular practice. The core members of crisis response teams in municipalities invest limited time and resources in team training; thus, it is essential that their training is effective and grounded in relevant risk and vulnerability analyses and evidence-based problems in municipalities. The study recommends the development of competencies in local emergency response teams based on theoretical frameworks of experiential team learning and shared mental model research.
An Affordable, Capable Maritime Emergency Response System
Responding to emergencies for coastal communities in remote regions often involves a maritime (Coast Guard) component. However, Coast Guard ships can be few in number, widely dispersed, difficult to crew, expensive to operate and build. A solution to this problem from a Naval perspective has been proposed by the US Naval Postgraduate School; a relatively small and inexpensive vessel, lightly crewed operating with a flotilla of uncrewed surface vessels. From a Coast Guard perspective, this could assist with response to coastal community, offshore infrastructure and maritime emergencies, while at the same time provide maritime domain awareness, enhance coastal sovereignty, protect critical underwater infrastructure (an increasingly important part of life in the Arctic and worldwide) and respond to maritime search and rescue and pollution incidents. The Need, the Problem, a Solution and its Technology are explored. Potential applications are described.
Generating Realistic Passenger Name Records with Privacy Compliance for Security Analysis
Passenger Name Record (PNR) data is essential for transportation analysis and security research, particularly in surveillance and threat detection. However, stringent security and privacy concerns limit access to real PNR data. This study presents a methodology for generating synthetic PNR data that not only replicates statistical properties but also reconstructs passenger social networks, models travel behaviours and preserves individual travel histories for security and mobility analysis. Our approach generates detailed, individual-level data—including passengers, bookings, and flights—while maintaining spatial, temporal, and chronological consistency to ensure realistic movement patterns while upholding privacy. This methodology offers a privacy-preserving alternative for transportation security and behavioural research, expanding access to high-quality data for future studies.
Supporting the Management of Spontaneous Volunteers Through AI-generated Task Descriptions
Since about 2015, smartphones apps have been used in some areas of Germany to coordinate first responders in case of emergencies, and crises. Initially, the focus of such applications were medical emergencies and sudden cardiac arrests in particular. Later, use cases for crowdtasking apps have been broadened in order to address all types of disaster scenarios, and tasks. Additionally, disaster and emergency managers use such tools nowadays to coordinate untrained, spontaneous volunteers without specific qualifications in times of crises. While those volunteers can be of valuable support for some disaster management tasks, such as establishing emergency shelters, emergency management professionals have sometimes perceived the management of spontaneous helpers as burdensome in stressful situations. Furthermore, in times of crises, some emergency managers also felt overwhelmed by the situation and would have needed more guidance. Artificial intelligence could provide tools and features to support emergency managers in the process of managing spontaneous volunteers. This paper presents some initial results from a desktop exercise where generative AI was used to support the creation of task descriptions for spontaneous volunteers, and discusses future uses of AI with regard to volunteer management in disaster situations.
Best Practices for Integrated Wildfire Information Management: Lessons from the 2024 Season, British Columbia, Canada
This research presents core components for an integrated, holistic approach to wildfire information and planning in northern British Columbia (BC), developed through partnerships with organizations, stakeholders, and at-risk communities. The approach focuses on providing timely, accurate, and culturally appropriate wildfire and air quality information to vulnerable communities, especially in remote and rural areas. Key elements include the deployment of automated air quality sensors and the use of R programming for efficient information dissemination. Through iterative collaboration and seasonal evaluations, best practices for knowledge translation and community-specific communication strategies have been established. The work-in-progress research also highlights the importance of community consent, feedback, and tailored messaging to improve comprehension and decision-making. Seasonal evaluations and impact assessment methods further refine the action plans for future wildfire seasons, ensuring continuous improvement in addressing the needs of at-risk populations. The study provides valuable ongoing insights for wildfire management both in Canada and globally, emphasizing collaborative, community-driven approaches.
Risk Modelling for Remote Communities: An Inuit-driven Bayesian Network Approach to Enhance Search and Rescue Operations in Arctic Canada
The Canadian Arctic's vast and unforgiving landscape presents unique challenges for Search and Rescue (SAR) operations, particularly in supporting the SAR volunteers in remote Inuit communities who face extreme conditions with limited resources. This study addresses the need for a culturally informed, probabilistic model that can enhance SAR effectiveness in Nunavut and Nunavik by enabling data-driven strategic planning and resource allocation. The paper introduces a novel Bayesian Network (BN) risk model that aims to capture the complexities involved in the Arctic ground SAR system. The model, developed through extensive community engagement, highlights the interdependencies between environmental conditions, resource availability, and SAR outcomes. By incorporating local knowledge and addressing systemic risks, the BN model offers a quantitative framework for SAR decision-making and policy development, aiming to improve the safety and resilience of Northern communities in the face of climate change and evolving geopolitical challenges. This work contributes to the wider SAR literature by offering a replicable approach for risk assessment and decision-making rooted in community expertise.
Wildfire Detection and Monitoring: A Drone-Based Approach and Comparative Analysis
Wildfires are an increasing global threat, exacerbated by climate change. Traditional detection methods, such as ground patrols and satellites, often face delays, limited coverage, or high costs. This paper presents Evolonic, an autonomous drone-based wildfire detection system developed by Fraunhofer IISB, Fraunhofer IIS and Friedrich- Alexander University Erlangen-Nuremberg. The system integrates a fixed-wing VTOL UAV, computer vision- based smoke detection, a web-based alert platform, and an automated base station for continuous operations. A comparative analysis with existing detection technologies (sensor networks, cameras, and satellites) highlights the advantages of UAV-based monitoring in detection speed, flexibility, and real-time verification. Initial results confirm its potential for rapid wildfire detection and improved emergency response. While challenges remain, such as regulatory constraints and nighttime detection, this research demonstrates UAVs' role in enhancing wildfire monitoring and response, offering a scalable and adaptable solution to mitigate wildfire risks.
Stakeholder Engagement Drivers: Insights from an Information System Innovation Project Supporting Wildfire Evacuation in Greece
The Samaria Gorge, a tourist destination in Crete, faces wildfire risk due to its dense forest, topography, number of visitors, and climate. As part of a H2020 project, the Gorge serves as a testing pilot for a new wildfire management platform that integrates technologies to improve fire detection, risk assessment, and resource allocation during a wildfire evacuation. A central component of the project is a multi-stakeholder network, which supports both governance and acceptance of solutions. To understand the drivers of network formation, we used a Social Network Analysis and Exponential Random Graphs Model approaches to identify the drivers of stakeholder collaboration. Our findings indicate that sectoral and wildfire management focus phase are factors driving connections, while the working in intersecting jurisdictions is not. The results highlight the challenges of multi-stakeholder collaboration, suggesting that policy frameworks and information systems need to develop specific mechanisms to encourage stakeholders to bridge collaborative gaps.
Applying Fuzzy Logic to Evaluate C2 Systems - Fusion of Heterogeneous Input Data
The comprehensive evaluation of command and control (C2) systems is a crucial factor in identifying strengths, weaknesses, and potential for improvement in such systems. However, research on such systems has shown how different and nonlinear the data in C2 system evaluation can be. These heterogeneous characteristics of data make it very difficult to combine the different variables and calculate new values for an evaluation result. After a detailed search, fuzzy logic was identified as a suitable mathematical approach that meets the requirements for calculations of different variables within an evaluation system for C2. Fuzzy logic in combination with fuzzy trees proves to be a good method to combine different input variables from a C2 system evaluation and to apply logic rules to represent and calculate dependencies. This paper explains the applicability of fuzzy logic in the context of C2 systems and shows examples from a fuzzy C2 system evaluation.
Social Media Use by Canadian Law Enforcement Agencies
This paper examines the presence of Canadian law enforcement agencies on social media, specifically Facebook, X, Instagram, and YouTube. It presents findings on when these social media accounts were created and how well they are received in terms of followers or subscribers. The paper further explores the roles and responsibilities of the Canadian law enforcement agencies with the most followers on social media at the federal, provincial, and municipal levels, highlighting notable examples of the positive impact these agencies are making by being active on social media. The paper also discusses the evolving role of the Canadian law enforcement agencies in the context of the rapidly changing social media landscape.
A Comparison of Ambulance Travel Time Approximation: Using Google Maps and Machine Learning
Accurately estimating ambulance travel time is critical for emergency medical services (EMS) to ensure timely response and efficient resource allocation. This study explores machine learning-based approaches for predicting ambulance travel time, comparing three artificial neural networks (ANN), Google Maps, and the traditional KWH constant speed model. Using real-world data from Nova Scotia's EMS system, we develop and evaluate these models based on prediction accuracy, computational efficiency, and applicability in both real-time and modeling contexts. Our results show that ANN model aligns closely with historical data, making it more suitable for EMS simulation and decision-making, despite requiring more complex feature inputs. This study highlights the strengths and limitations of each method and offers insights into selecting the most appropriate approach based on EMS application requirements.
Bridging Theory and Practice for Enhanced Cybersecurity Awareness in Critical Infrastructures
Developing advanced cybersecurity training programs is crucial for increasing cybersecurity awareness, especially when dealing with Critical Infrastructures (CIs). There is a significant gap in addressing both theoretical and practical activities to build a comprehensive and effective program that matches real-life cyberattacks on critical systems. This paper presents the efforts undertaken within the EU project CyberSecPro to bridge this gap by providing advanced training materials that align theoretical insights with practical activities to handle real scenarios.
Exploring the Interplay Between Threat Appraisal, Efficacy Appraisal, and Behavior Change During a “Once-in-a-Lifetime” Storm
In 2022, Hurricane Fiona made landfall in Atlantic Canada, causing widespread destruction across the region. The storm provided the opportunity to test the Extended Parallel Process Model (EPPM), which predicts that warning messages influence threat appraisal and efficacy appraisal, and in turn influence whether individuals engage in fear control processes or danger-control processes. To do so, a questionnaire was disseminated to residents of Atlantic Canada (n=582) approximately two weeks after the storm. The results find that respondents generally fell in one of two protective action classes: low protective action and high protective action. Efficacy appraisal had no effect on whether or which actions respondents took during the storm. In contrast, anxiety and information seeking were both found to be significant. The results seemingly contradict previous research on fear appeals guided by the EPPM. In our conclusion, we speculate on why this may be and recommend several opportunities for future research.
How Critical is Critical? Towards a Decision Framework for Disaster-specific Critical Entities
During recent disaster and crisis events, it became more and more evident that critical infrastructures are of utmost importance for a quick recovery from these events. However, not all infrastructures might be equally critical in a crisis scenario; some organizations might be more critical during some disasters than during others. Additionally, due to current regulation, even more organizations are categorized as “critical”, which makes it more difficult to the most relevant ones during a crisis. In this paper, we present an identification and classification approach for the importance of critical entities during a disaster or incident. This approach is related to standard risk and resilience management processes and can be directly integrated into the first phases of these processes. In particular, it takes the nature of the disaster as well as of the respective entity into account and sets them in relation to each other. Additionally, it also considers the economic and societal role of the individual entity, i.e., their importance for different industry sectors and their supply chains as well as for the well-being of society in general. To show how the decision process would work in a realistic scenario, we apply it for a pandemic scenario and a supply shortage scenario. Additionally, we also generally discuss how the process can be applied in other domains.
Ethical and Safety Concerns for UAV-Assisted Data Collection for Disaster Management
Emergency scenarios threaten human survival and well-being, requiring proactive risk assessment, hazard identification, and swift responses. In addition to several essential services, communication systems are very much useful for information exchange and situational assessment but are often disrupted by landslides. Unmanned aerial vehicles (UAVs), with their 3D maneuverability, can be deployed to effectively restore connectivity and collect safety data during such events. Key challenges include optimizing UAV placement as base stations and designing optimized trajectories for data collection, ensuring sufficient channel gain between UAVs and users. Estimating a propagation channel map through location-based measurements offers a potential solution. However, deploying UAVs to gather information raises ethical concerns, including safety, privacy, and moral implications. Addressing these requires a technoethics framework grounded in principles like beneficence, non-maleficence, autonomy, justice, and explicability. A literature review and expert consultations can provide critical insights to navigate these challenges.
An Agent-based Exploration of Information Sharing for Adaptation in Crisis Response
Humanitarian and military organizations face deeply uncertain, continuously changing environments due to disasters and conflict. Information sharing is vital to adapt to these disruptions effectively and ensure the timely availability of essential equipment and supplies anywhere in the world. However, little is known about the role of information sharing in adapting to a changing environment. We use an agent-based discrete-event simulation to study information-sharing mechanisms, specifically delayed information-sharing behavior, and analyze how they impact the adaptation of decision-making structures over time. Drawing upon adaptation models from literature, we develop a model in which agents share information and endogenously create these structures. We experiment with various levels of information delays in dynamically changing environments and assess how this affects adaptation and performance. Our findings unveil that horizontal delays lead to earlier hierarchical expansion and vertical delays slow decision-making in crisis response environments.
The Future of Crisis Response Training: AI-Generated Feedback for Incident Commanders?
In today’s complex crisis landscape, effective incident command training relies on dynamic decision-making assessment frameworks. However, the Effective Command Behavioral Marker Framework (EC) momentarily demands a lot from human assessors, who must evaluate 72 criteria across a 5-point scale, leading to excessive cognitive load. This study explores whether AI-generated written feedback can support in the future assessors and possibly enhance learning outcomes by providing structured, data-driven insights of command training. We examine the assessment results from 85 incident commanders solutions to a virtual simulation ”School Fire” scenario. Key challenges brought out in the feedback included delayed decision-making, inter-agency coordination gaps, and situational awareness deficits. While expert feedback is valuable, the time constraints for compiling the written feedback create a discrepancy in the length and quality of the provided feedback. This study explores how AI can complement human assessor’s by reducing cognitive overload and enhancing incident command training through structured, data-driven feedback that supports assessors s expert judgment. Dynamic decision-making feedback systems using Human-AI collaboration may redefine training methodologies for next-generation incident commanders.
Effective Command Behavioral Marker Framework (EC)
Virtual simulation-based training (VSBT)
Complexity Level of Virtual Simulation Scenarios for Command and Control Behaviors Assessment
This ongoing evaluates the command-and-control (C2) activities of rescue incident commanders. The study analyzes the assessment results of 382 commanders’ C2 behaviors using virtual simulation scenarios in two cases and compares them with other scenarios created through the Collaborative Authoring Process Model for Virtual Simulation Scenarios (CAPM). In the first scenario, involving a chemical leak in a hangar, 60 working incident commanders participated. In the second scenario, 83 commanders responded to a more complex case featuring fire spread and multiple casualties in a care home. Scenario complexity increases with the number of victims, key individuals, and the size of the building. When scenarios become overly complicated for first-level command, the standard ranking of common C2 behaviors is not maintained. To guide the development of future virtual simulation scenarios, recommendations for situational elements should be tailored to each command level, ensuring that the scenario storyline aligns with the appropriate level of likelihood and complexity.
Collaborative Authoring Process Model for Virtual Simulation Scenarios (CAPM)
Community Climate Resilience Building: A Case Study of Tidal Flooding in Kerala, India
Sea level rise due to climate change threatens coastal communities, causing coastal flooding and other extreme weather events. This study explores a community-based approach integrated with scientific data collection methods to develop bottom-up strategies for building climate resilience, focusing on tidal flooding in the lowland coastal plains of Ernakulam district, Kerala. Community engagement was facilitated through a multilayered approach by collaborating with grassroots organizations and different tiers of government. Through low-cost, decentralized data collection methods, affected regions and deviations from expected tidal impact patterns were identified. Open-ended interviews revealed traditional adaptive strategies practiced by the community that are effective in guiding scientific planning and development to build local, context-aware resilience in coastal regions. The results of this study demonstrate that continuous community engagement, participatory methodologies and reciprocal learning are critical in developing sustainable solutions to tidal flooding and in empowering communities to advocate for their own solutions.