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Flavio Dusse, Renato Novais, & Manoel Mendonça. (2020). A Visual Analytics Based Model for Crisis Management Decision-Making. In Amanda Hughes, Fiona McNeill, & Christopher W. Zobel (Eds.), ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management (pp. 157–166). Blacksburg, VA (USA): Virginia Tech.
Abstract: Crisis Management (CM) refers to the ability to deal with crisis tasks in different phases and iterations. People working in a crisis are generally under pressure to make the right decision at the right time. They must process large amounts of data and assimilate the received information in an intuitive way. Visual Analytics (VA) is potentially useful to analyze and understand the huge amount of data in several areas including in a crisis. We propose a model based on VA to support decision-making in CM. The aim of the model is to help visualization designers to create effective VA interfaces, to help crisis managers to make quick and assertive decisions with them. In previous studies, we carried out a survey protocol with a multi-method approach to collect data on crisis related decision-making and analyze all these data qualitatively with formal techniques during the large events held in Brazil in recent years. In this work, we used our previous findings to develop the proposed model. We validated it using the focus group technique. With the new findings, we identified relevant insights on the use of VA for crisis management. We hope that, with these continuous cycles of validation and improvement, the agencies that manage crises might use our model as a reference for building more effective IT decision-making infrastructures based on VA.
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Florent Dubois, Paul Renaud-Goud, & Patricia Stolf. (2022). Dynamic Capacitated Vehicle Routing Problem for Flash Flood Victim’s Relief Operations. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 68–86). Tarbes, France.
Abstract: Flooding relief operations are Dynamic Vehicle Routing Problems (DVRPs). The problem of people evacuation is addressed and formalized in this paper. Characteristics of this DVRP problem applied to the crisis management context and to the requirements of the rescue teams are explained. In this paper, several heuristics are developed and assessed in terms of performance. Two heuristics are presented and adapted to the dynamic problem in a re-optimization approach. An insertion heuristic that inserts demands in the existing plan is also proposed. The evaluation is conducted on various dynamic scenarios with characteristics based on a study case. It reveals better performances for the heuristics with a re-optimization approach.
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Gabriel, A., & Torres, F. S. (2023). Navigating Towards Safe and Secure Offshore Wind Farms: An Indicator Based Approach in the German North and Baltic Sea. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 609–619). Omaha, USA: University of Nebraska at Omaha.
Abstract: Offshore wind farms (OWFs) have become an increasingly relevant form of renewable energy in recent years, with the German North Sea being one of the most active regions in the world. However, the safety and security of OWF have become increasingly important due to the potential threats and risks associated with their growing share in the security of energy supply. This paper aims to present a comprehensive and systematic indicator-based approach to assess the safety and security status of OWFs in the German North Sea. The approach is based on the results of a survey of people working in the offshore industry and draws on the work published by Gabriel et al. (2022). The results of the study suggest that the indicator-based approach is a useful tool for end users to assess the security status of offshore wind farms and can be used for further research and development.
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Gerhard Rauchecker, & Guido Schryen. (2018). Decision Support for the Optimal Coordination of Spontaneous Volunteers in Disaster Relief. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 69–82). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: When responding to natural disasters, professional relief units are often supported by many volunteers which are not affiliated to humanitarian organizations. The effective coordination of these volunteers is crucial to leverage their capabilities and to avoid conflicts with professional relief units. In this paper, we empirically identify key requirements that professional relief units pose on this coordination. Based on these requirements, we suggest a decision model. We computationally solve a real-world instance of the model and empirically validate the computed solution in interviews with practitioners. Our results show that the suggested model allows for solving volunteer coordination tasks of realistic size near-optimally within short time, with the determined solution being well accepted by practitioners. We also describe in this article how the suggested decision support model is integrated in the volunteer coordination system, which we develop in joint cooperation with a disaster management authority and a software development company.
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Hager, F., Reuter-Oppermann, M., Müller, T., & Ottenburger, S. (2023). Towards the Design of a Simulation-based Decision Support System for Mass-Casualty Incidents. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 565–574). Omaha, USA: University of Nebraska at Omaha.
Abstract: In case of a mass-casualty incident, e.g. due to a disaster, a high number of patients need medical care within a short time frame and often, a significant percentage must be transported to a hospital or another suitable care facility. Then, different mass transportation modes (e.g., busses, ships or trains) may be used to quickly transport patients to available medical treatment centres outside of the disaster area. Within the SimPaTrans project, we develop a simulation-based decision support system for locating, sizing and analysing different modes of transport in order to prepare for mass-casualty incidents in Germany. In this paper, we present the outline of the tool as well as a first optimisation use case for transportation patients within the city of Karlsruhe, Germany
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Haitao Sun, Zhiru Wang, Guofeng Su, & Jianguo Chen. (2016). Topological Structure Vulnerability Assessment of Shanghai Urban Metro Networks. In A. Tapia, P. Antunes, V.A. Bañuls, K. Moore, & J. Porto (Eds.), ISCRAM 2016 Conference Proceedings ? 13th International Conference on Information Systems for Crisis Response and Management. Rio de Janeiro, Brasil: Federal University of Rio de Janeiro.
Abstract: Topological structure vulnerability assessment approach for Urban Metro Networks (UMNS) was proposed in order to decrease the impact caused by incidents. Failure scale of stations and sections random failure and target attacks was evaluated. The results show that UMNS is more vulnerable to target attacks on stations than random failure on stations. But UMNS is less vulnerable to target attacks on sections than random failure on sections. Additionally, UMNS is more vulnerable to station failure than sections. It could be concluded as more resources should be put on big transfer stations in UMNS operation management to avoid large scale impacts. The proposed methodology is not intended to predict the occurrence of events but rather to be used a management tool. Results from the evaluation are valuable elements in planning UMNS. They can be used for network planning, further detailed hazard studies, deciding on the arrangement of emergency resources.
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Haya Aldossary, & Graham Coates. (2019). A Preliminary Optimisation-based Approach to Coordinate the Response of Ambulances in Mass Casualty Incidents. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management. Valencia, Spain: Iscram.
Abstract: Mass Casualty Incidents (MCIs) may occur with no notice and require a rapid response to manage the casualties and arrange their transportation to hospitals. MCIs may result in different numbers of casualties and fatalities. Further, response time can play a crucial role in reducing fatalities and protecting lives. This paper reports on a preliminary optimisation-based approach, termed MCIER, which has been developed to co-ordinate the response of ambulances to multiple MCIs. In this approach, a realistic representation of the road network is modelled for the geographical area of interest. Also, a Neighbourhood Search Algorithm (NSA) has been developed in order to find the optimum solution to the problem under consideration. A hypothetical case study of a MCI in Newcastle-upon-Tyne has been considered to investigate the effect on response time of the time of day, and day of week, on which the incident occurs.
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Xiaofeng Hu, Shifei Shen, & Jiansong Wu. (2012). Modeling of attacking and defending strategies in situations with intentional threats. In Z.Franco J. R. L. Rothkrantz (Ed.), ISCRAM 2012 Conference Proceedings – 9th International Conference on Information Systems for Crisis Response and Management. Vancouver, BC: Simon Fraser University.
Abstract: Intentional threats including terrorism have become a worldwide catastrophe risk since recent years. To protect the cities from being attacked, the macro-level study of decision analysis should be given more considerations. In this paper, we proposed a model for describing the strategic game between attackers and defenders based on the methodology of matrix game. This model can be employed to determine which target will be selected by attackers and which attacking strategy and defending strategy will be chosen by attackers and defenders respectively. Furthermore, the defenders of the city can use this model to set priorities among their defending strategies. The importance of this work is to establish a reasonable framework for modeling the attacking and defending strategies rather than assessing the real risk of urban targets, so the model is illustrated by using fictitious numbers. The model proposed in this paper can provide scientific basis for macroscopic decision making in responding to intentional threats. © 2012 ISCRAM.
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Ivar Svare Holand, Peter Mozelius, & Trond Olav Skevik. (2021). A structured and dynamic model for emergency management exercises. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 186–197). Blacksburg, VA (USA): Virginia Tech.
Abstract: Emergencies are management challenges, and emergency exercises that involve multiple collaborating parties is a means towards mastering them. Such exercises are often conducted in a virtual training environment based on complex disaster scenarios. The reported study was carried out using a requirement-focused design approach. The aim was to describe and discuss a relevant design for lean, dynamic, and cost-efficient emergency management exercise systems. Data were gathered from a literature study and analyses of earlier emergency management projects in which the authors had participated. Despite the complexity of many current emergency management exercises, the scenarios usually involve only the response phases and have a linear structure that hinders both didactic aspects and the software structure. The conclusion drawn from the study is that an emergency management exercise model should focus on managing the activities that correspond to alternatives that unfold from a dynamic scenario. Finally, the authors recommend the principles of alternate reality games as a way towards more dynamic and cost-efficient emergency exercise systems.
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Jacob L. Graham, & Mark B. Stephens. (2018). Analytic Decision Gaming – A Tool to Develop Crisis Response and Clinical Reasoning. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 60–68). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Emerging threats provide motivation to develop new methods for preparing the next generation of crisis responders. Bayesian theory shifts reasoning toward a probabilistic, epistemic paradigm, giving rise to Evans' revised heuristic-analytic theory. Researchers at The Pennsylvania State University use scenario-based training and the analytic decision game (ADG) to blend and implement these processes as foundational pedagogy for engaging, educating and training medical students as crisis responders and critical thinkers. The ADG scenarios vary by content and level of expertise, lending themselves readily adaptable to both crisis response preparation and the development of clinical reasoning. The ADG creates a virtual crisis requiring participants to engage in scenario management as role-players. For the past two years, medical students from the Penn State College of Medicine, in their first year of training, have participated in the ADG Lights Out scenario, testing community preparation and resilience after a wide-spread and months-long power outage.
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Juan Francisco Carías, Leire Labaka, Jose Maria Sarriegi, Andrea Tapia, & Josune Hernantes. (2019). The Dynamics of Cyber Resilience Management. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management. Valencia, Spain: Iscram.
Abstract: With the latent problem of security breaches, denial of service attacks, other types of cybercrime, and cyber incidents in general, the correct management of cyber resilience in critical infrastructures has become a high priority. However, the very nature of cyber resilience, requires managing variables whose effects are hard to predict, and that could potentially be expensive. This makes the management of cyber resilience in critical infrastructures a substantially hard task.
To address the unpredictability of the variables involved in managing cyber resilience, we have developed a system dynamics model that represents the theoretical behaviors of variables involved in the management of cyber resilience. With this model, we have simulated different scenarios that show how the dynamics of different variables act, and to show how the system would react to different inputs.
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Julian Zobel, Patrick Lieser, Tobias Meuser, Lars Baumgärtner, Mira Mezini, & Ralf Steinmetz. (2021). Modeling Civilian Mobility in Large-Scale Disasters. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 119–132). Blacksburg, VA (USA): Virginia Tech.
Abstract: When disasters destroy critical communication infrastructure, smartphone-based Delay-Tolerant Networks (DTNs) can provide basic communication for civilians. Although field tests have shown the practicability of such systems, real-world experiments are expensive and hardly repeatable. Simulations are therefore required for the design and extensive evaluation of novel DTN protocols, but meaningful assertions require realistic mobility models for civilians. In this paper, trace files from a large-scale disaster field test are analyzed to identify typical human behavior patterns in a disaster area. Based on this, we derive a novel civilian disaster mobility model that incorporates identified behaviors such as group-based movement and clustering around points-of-interests such as hospitals and shelters. We evaluate the impact of mobility on DTN communication performance by comparing our model with other established mobility models as well as the trace file dataset in a simulative evaluation based on the field test scenario. In general, our mobility model leads to a more realistic assessment of DTN communication performance compared to other mobility models.
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Julian Zobel, Ralf Kundel, & Ralf Steinmetz. (2022). CAMON: Aerial-Ground Cooperation System for Disaster Network Detection. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 87–101). Tarbes, France.
Abstract: Information on large-scale disaster areas, like the location of affected civilians, is highly valuable for disaster relief efforts. This information can be collected by an Aerial Monitoring System, using UAVs to detect smart mobile devices carried by civilians. State-of-the-art systems typically rely on a purely passive detection approach. In this paper, we present a cooperative communication system between UAVs and ground-based devices to improve the detection performance of such an Aerial Monitoring System. We provide different approaches for the cooperative information collection and evaluate them in a simulated inner-city scenario. The results highlight the effectiveness of the cooperative system, being able to detect civilian devices in the disaster area faster and more comprehensively than a non-cooperative approach.
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Julius Bañgate, Julie Dugdale, Carole Adam, & Elise Beck. (2017). A Review on the Influence of Social Attachment on Human Mobility During Crises. In eds Aurélie Montarnal Matthieu Lauras Chihab Hanachi F. B. Tina Comes (Ed.), Proceedings of the 14th International Conference on Information Systems for Crisis Response And Management (pp. 110–126). Albi, France: Iscram.
Abstract: Human behaviour during crisis evacuations is soial in nature. In particular, social attachment theory posits that proximity of familiar people, places, objects, etc. promotes calm and a feeling of safety, while their absence triggers panic or flight. In closely bonded groups such as families, members seek each other and evacuate as one. This makes attachment bonds necessary in the development of realistic models of mobility during crises. In this paper, we present a review of evacuation behaviour, theories on social attachment, crises mobility, and agent-based models. We found that social attachment influences mobility in the dierent stages of evacuation (pre, during and post). Based on these findings, we intend to develop a multi-agent model of mobility during seismic crises, using the belief, desire and intention (BDI) agent architecture.
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Kishimoto, M., Osaragi, T., & Chan Yili. (2023). Evaluation of Improvement Projects in Densely Built-Up Area using a Large Earthquake Disaster Simulator: A case study in Kyojima Area, Tokyo. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 546–564). Omaha, USA: University of Nebraska at Omaha.
Abstract: This paper aims to (1) evaluate the disaster mitigation effects of improvement projects in a certain area and (2) provide a basis for strategic planning to promote further improvements. Specifically, we decompose local improvements in the analyzed area into multiple scenarios and examine their effects and issues. First, we describe the “large earthquake disaster simulator,” which estimates property damage and human casualties in a large earthquake. Then, the Kyojima area of Sumida-Ku, Tokyo, is selected as the analyzed area. We decompose the improvement projects implemented during 2006 – 2016 and prepare six scenarios. Finally, a simulation analysis is conducted. We demonstrate that fire spread could be effectively blocked by (1) ensuring sufficient road width and (2) identifying the critical buildings in terms of fire spread mitigation and making them fireproof.
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Laura Szczyrba, Yang Zhang, Duygu Pamukcu, & Derya Ipek Eroglu. (2020). A Machine Learning Method to Quantify the Role of Vulnerability in Hurricane Damage. In Amanda Hughes, Fiona McNeill, & Christopher W. Zobel (Eds.), ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management (pp. 179–187). Blacksburg, VA (USA): Virginia Tech.
Abstract: Accurate pre-disaster damage predictions and post-disaster damage assessments are challenging because of the complicated interrelationships between multiple damage drivers, including various natural hazards, as well as antecedent infrastructure quality and demographic characteristics. Ensemble decision trees, a family of machine learning algorithms, are well suited to quantify the role of social vulnerability in disaster impacts because they provide interpretable measures of variable importance for predictions. Our research explores the utility of an ensemble decision tree algorithm, Random Forest Regression, for quantifying the role of vulnerability with a case study of Hurricane Mar\'ia. The contributing predictive power of eight drivers of structural damage was calculated as the decrease in model mean squared error. A measure of social vulnerability was found to be the model's leading predictor of damage patterns. An additional algorithm, other methods of quantifying variable importance, and future work are discussed.
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Lauren Bateman, & Erica Gralla. (2018). Evaluating Strategies for Intra-Organizational Information Management in Humanitarian Response. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 145–157). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Information management is critical in humanitarian response, yet intra-organizational information management practices have not been well-studied. This paper evaluates several strategies for intra-organizational information management. An agent-based model represents the dynamics of information-gathering and -sharing, in order to examine the impact of each strategy on (1) the time required to acquire adequate information for decision-making and (2) the amount of excess information acquired in the process. The results show that holding regular information-sharing meetings significantly reduces the time to acquire adequate information, but does not reduce information overload; however, deploying an information management specialist reduces both time required and information overload. The results support recommendations for humanitarian organizations deciding how to improve their internal information management approaches.
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Lida Huang, Guoray Cai, Hongyong Yuan, Jianguo Chen, Yan Wang, & Feng Sun. (2018). Modeling Threats of Mass Incidents Using Scenario-based Bayesian Network Reasoning. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 121–134). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Mass incidents represent a global problem, putting potential threats to public safety. Due to the complexity and uncertainties of mass incidents, law enforcement agencies lack analytical models and structured processes for anticipating potential threats. To address such challenge, this paper presents a threat analysis framework combining the scenario analysis method and Bayesian network (BN) reasoning. Based on a case library
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Maël Arnaud, Carole Adam, & Julie Dugdale. (2017). The role of cognitive biases in reactions to bushfires. In eds Aurélie Montarnal Matthieu Lauras Chihab Hanachi F. B. Tina Comes (Ed.), Proceedings of the 14th International Conference on Information Systems for Crisis Response And Management (pp. 85–96). Albi, France: Iscram.
Abstract: Human behaviour is influenced by many psychological factors such as emotions, whose role is already widely recognised. Another important factor, and all the more so during disasters where time pressure and stress constrain reasoning, are cognitive biases. In this paper, we present a short overview of the literature on cognitive biases and show how some of these biases are relevant in a particular disaster, the 2009 bushfires in the South-East of Australia. We provide a preliminary formalisation of these cognitive biases in BDI (beliefs, desires, intentions) agents, with the goal of integrating such agents into agent-based models to get more realistic behaviour. We argue that taking such “irrational” behaviours into account in simulation is crucial in order to produce valid results that can be used by emergency managers to better understand the behaviour of the population in future bushfires.
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Marco Polo Ruiz Herrera, & Juan Sánchez Díaz. (2019). Improving Emergency Response through Business Process, Case Management, and Decision Models. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management. Valencia, Spain: Iscram.
Abstract: The emergency procedures contain a set of actions responsible for providing the necessary corrective measures to address an emergency. The relevance of contextual knowledge during emergency responses is of utmost importance since many decisions are made from the information gathered in real time that sometimes conflicts with the formal knowledge specified in the emergency plan. Consequently, tools that support the emergency plan mentioned must be sensitive to context and allow decision making at the time an emergency takes place. We demonstrate how Case Management Modeling Notation (CMMN) along with Decision Model and Notation (DMN) are very suitable approaches to obtain a flexible model adapted to the context-driven response processes.
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Mark Parent, Jean-François Gagnon, Tiago H. Falk, & Sébastien Tremblay. (2016). Modeling the Operator Functional State for Emergency Response Management. In A. Tapia, P. Antunes, V.A. Bañuls, K. Moore, & J. Porto (Eds.), ISCRAM 2016 Conference Proceedings ? 13th International Conference on Information Systems for Crisis Response and Management. Rio de Janeiro, Brasil: Federal University of Rio de Janeiro.
Abstract: New technologies are available for emergency management experts to help them cope with challenges such as information overload, multitasking and fatigue. Among these technologies, a wide variety of physiological sensors can now be deployed to measure the Operator Functional State (OFS). To be truly useful, such measures should not only characterize the overall OFS, but also the specific dimensions such as stress or mental workload. This experiment aimed to (1) design a multi-dimensional model of OFS, and (2) test its application to an emergency management situation. First, physiological data of participants were collected during controlled experimental tasks. Then, a support vector classifier of mental workload and stress was trained. Finally, the resulting model was tested during an emergency management simulation. Results suggest that the model could be applied to emergency management situations, and leave the door open for its application to emergency response on the field.
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Maude Arru, & Elsa Negre. (2017). People Behaviors in Crisis Situations: Three Modeling Propositions. In eds Aurélie Montarnal Matthieu Lauras Chihab Hanachi F. B. Tina Comes (Ed.), Proceedings of the 14th International Conference on Information Systems for Crisis Response And Management (pp. 139–149). Albi, France: Iscram.
Abstract: Warnings can help to prevent damages and harm if they are issued timely and provide information that help responders and population to adequately prepare for the disaster to come. Today, there are many indicator and sensor systems that are designed to reduce disaster risks. These systems have proved to be eective. Unfortunately, as all systems including human beings, a part of unpredictable remains. Indeed, each person behaves dierently when a problem arises. In this paper, we focus on people behaviors in crisis situations: from the definition of factors that impact human behavior to the integration of these behaviors, with three dierent modeling propositions, into a warning system in order to have more and more eÿcient crisis management systems.
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Meshal Alharbi, & Graham Coates. (2019). Assessing Flood Recovery of Small Businesses using Agent-Based Modelling and Simulation. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management. Valencia, Spain: Iscram.
Abstract: In developed countries, small and medium-sized enterprises (SMEs) represent the majority of all businesses, e.g. 99.9% in the UK. Given this significant proportion, any disruption to the operation of SMEs will have a negative impact on a nation?s economy. In the context of flooding, this paper reports on the use of agent-based modelling and simulation (ABMS) to assess SMEs immediate response and short-term recovery. In particular, it focuses on the interactions between manufacturing SMEs and mutual aid partners, and retail SMEs and companies specializing in refurbishing premises. Results show that a manufacturing SME with a mutual aid partner can reduce loss in production by approximately 6% over a 7 working day period. In relation to retail
SMEs, those with employees able to be allocated to refurbish its premises recovered faster than SMEs employing a refurbishment company, potentially one day earlier.
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Michael Bartolacci, & Stanko Dimitrov. (2016). A Network Interdiction Perspective for Providing Emergency Communications: An Analysis for Promoting Resiliency Subject to Resource Constraints and Security Concerns. In A. Tapia, P. Antunes, V.A. Bañuls, K. Moore, & J. Porto (Eds.), ISCRAM 2016 Conference Proceedings ? 13th International Conference on Information Systems for Crisis Response and Management. Rio de Janeiro, Brasil: Federal University of Rio de Janeiro.
Abstract: Disasters, whether natural or manmade, and other types of emergencies create the need for immediate and secure communications between and among the affected populace, governmental agencies, non-governmental organizations (NGOs) and other types of emergency responders. It is through these communications that the affected populace is able to show resilient behavior, both psychologically and economically. A network interdiction model is proposed that can be utilized to create a more reliable design for such a communications network against the motives of would-be attackers whose aim it is to disrupt emergency communications and inflict damage on the affected populace. The contribution of this work is the application of the network interdiction modeling framework to an emergency communication scenario.
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Milad Baghersad, Christopher W. Zobel, & Ravi Behara. (2020). Evaluation of Local Government Performance after Disasters. In Amanda Hughes, Fiona McNeill, & Christopher W. Zobel (Eds.), ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management (pp. 210–217). Blacksburg, VA (USA): Virginia Tech.
Abstract: Monitoring and evaluation can help organizations involved in disasters learn from their responses to prior events and improve their performance over time. Using a data set of non-emergency service requests in New York City (NYC), this paper provides a method to evaluate and compare the performance of local governments in terms of service request response times after different disaster events. In particular, the proposed method can be used to compare such performance across divisions or boroughs in a city. To illustrate this, we evaluate the performance in five of NYC's boroughs: the Bronx, Brooklyn, Manhattan, Queens, and Staten Island, across seven major natural disaster events from 2010 to 2012. Our analyses show that Queens and Brooklyn demonstrate better performance than the other boroughs in almost all of the seven events under consideration.
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