Aibek Musaev, De Wang, & Calton Pu. (2014). LITMUS: Landslide detection by integrating multiple sources. In and P.C. Shih. L. Plotnick M. S. P. S.R. Hiltz (Ed.), ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management (pp. 677–686). University Park, PA: The Pennsylvania State University.
Abstract: Disasters often lead to other kinds of disasters, forming multi-hazards such as landslides, which may be caused by earthquakes, rainfalls, water erosion, among other reasons. Effective detection and management of multihazards cannot rely only on one information source. In this paper, we evaluate a landslide detection system LITMUS, which combines multiple physical sensors and social media to handle the inherent varied origins and composition of multi-hazards. LITMUS integrates near real-time data from USGS seismic network, NASA TRMM rainfall network, Twitter, YouTube, and Instagram. The landslide detection process consists of several stages of social media filtering and integration with physical sensor data, with a final ranking of relevance by integrated signal strength. Applying LITMUS to data collected in October 2013, we analyzed and filtered 34.5k tweets, 2.5k video descriptions and 1.6k image captions containing landslide keywords followed by integration with physical sources based on a Bayesian model strategy. It resulted in detection of all 11 landslides reported by USGS and 31 more landslides unreported by USGS. An illustrative example is provided to demonstrate how LITMUS' functionality can be used to determine landslides related to the recent Typhoon Haiyan.
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Douglas C. Pattie, & Stefanie Dannenmann. (2008). Evaluation and strengthening of early warning systems in countries affected by the 26 December 2004 Tsunami. In B. V. de W. F. Fiedrich (Ed.), Proceedings of ISCRAM 2008 – 5th International Conference on Information Systems for Crisis Response and Management (pp. 415–423). Washington, DC: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: The objective of this international initiative was to provide an integrated framework for strengthening early warning systems in the Indian Ocean region by building on existing systems and facilitating coordination among specialized and technical institutions. The project supported the development of tsunami early warning systems in collaboration with numerous United Nations and other organizations devoted to disaster risk management and risk reduction. For the practitioner of early warning systems, the project has been divided into two areas-warning system development and preparedness. As a cross-cutting theme, the project promoted multi-hazard end-to-end systems in a regional context by emphasizing (i) risk knowledge, (ii) monitoring and warning service, (iii) communications and dissemination of understandable warnings and (iv) response capability and preparedness. The activities of the project were structured into five components-system implementation, integrated risk management, public awareness and education, community-level approaches and project coordination. Practitioners should note that the work represents a first step for establishing a complete tsunami early warning system within a multi-hazard framework.
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Paulini, M. S., Duran, D., Rice, M., Andrekanic, A., & Suri, N. (2023). KENNEL Threat Detection Boxes for First Responder Situational Awareness and Risk Management. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 208–219). Omaha, USA: University of Nebraska at Omaha.
Abstract: KENNEL is a deployable IoT-based system consisting of a network of unattended ground sensors, known as Threat Detection Boxes (TDBs), which may be outfitted with any variety of custom and commercial-off-the-shelf sensors for hazard detection. The KENNEL system fills a technological gap for sensor fusion, interpretation, and real-time alerting via existing information management systems, such as Team Awareness Kit (TAK). First responders face a critical need for improved situational awareness, detection, and response to hazardous events. KENNEL provides a first of its kind, low-cost sensing & data fusion platform that is highly extensible, configurable, and self-sustaining, opening a world of modernization and innovation possibilities across the first responder domain. TDBs may also be statically or ad hoc deployed, improving flexibility, stand-off hazard detection, and resilience in the operational domain. From critical infrastructure monitoring to wearables, the system affords timeliness of critical information for effective risk management and increased personnel safety.
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Jaziar Radianti, Julie Dugdale, Jose J. Gonzalez, & Ole-Christoffer Granmo. (2014). Smartphone sensing platform for emergency management. In and P.C. Shih. L. Plotnick M. S. P. S.R. Hiltz (Ed.), ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management (pp. 379–383). University Park, PA: The Pennsylvania State University.
Abstract: The increasingly sophisticated sensors supported by modern smartphones open up novel research opportunities, such as mobile phone sensing. One of the most challenging of these research areas is context-aware and activity recognition. The Smart Rescue project takes advantage of smartphone sensing, processing and communication capabilities to monitor hazards and track people in a disaster. The goal is to help crisis managers and members of the public in early hazard detection, prediction, and in devising risk-minimizing evacuation plans when disaster strikes. In this paper we suggest a novel smartphone-based communication framework. It uses specific machine learning techniques that intelligently process sensor readings into useful information for the crisis responders. Core to the framework is a content-based publish-subscribe mechanism that allows flexible sharing of sensor data and computation results. We also evaluate a preliminary implementation of the platform, involving a smartphone app that reads and shares mobile phone sensor data for activity recognition.
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Tom Ritchey. (2006). Modeling multi-hazard disaster reduction strategies with computer-Aided morphological analysis. In M. T. B. Van de Walle (Ed.), Proceedings of ISCRAM 2006 – 3rd International Conference on Information Systems for Crisis Response and Management (pp. 339–346). Newark, NJ: Royal Flemish Academy of Belgium.
Abstract: Disaster Risk Management (DRM) is a multi-dimensional problem complex requiring knowledge and experience from a wide range of disciplines. It also requires a methodology which can collate and organize this knowledge in an effective, transparent manner. Towards this end, seven specialists from the social, natural and engineering sciences collaborated in a facilitated workshop in order to develop a prototype multi-hazard disaster reduction model. The model, developed with computer-Aided morphological analysis (MA), makes it possible to identify and compare risk reduction strategies, and preparedness and mitigation measures, for different types of hazards. Due to time constraints, the model is neither complete nor accurate-but only represents a proof-of-principle. The workshop was sponsored by the Earthquake Disaster Mitigation Research Center (EDM) in Kobe, in January, 2005.
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Maria A. Santos, António Gonçalves, Sandra Silva, Nuno Charneca, & Miguel Gamboa. (2004). Dam break emergency response Information System. In B. C. B. Van de Walle (Ed.), Proceedings of ISCRAM 2004 – 1st International Workshop on Information Systems for Crisis Response and Management (pp. 27–32). Brussels: Royal Flemish Academy of Belgium.
Abstract: Although considered of low risk, incidents with dams may cause significant damage both directly and indirectly. Direct losses are usually easier to assess (assuming human lives are quantifiable), but indirect losses are difficult to measure and may take some time before the original situation is restored. Disaster prevention and vulnerability reduction have been topics of major concern in many local, national or international organisations for some years. These can be accomplished through emergency management which begins with hazard identification and planning for disaster mitigation but encompasses other activities as risk analysis, risk response and recovery. Therefore, an emergency management system with capacity to: i) forecast critical situations; ii) warn the population as well as the authorities; and iii) support the civil protection system to deal with an emergency, is a most helpful tool to minimize the impact of an accident. The Information System described herein fulfils mainly the third objective, i.e. it is intended to help the Civil Protection System in Portugal, to respond to an emergency caused by the failure of a dam. It is an Internet-based application, which integrates all relevant data for the implementation of a dam emergency plan. These data include the main characteristics of the dam and its reservoir, the character-isation of the dam downstream valley as well as the response procedures to be followed in an emergency. © Proceedings ISCRAM 2004.
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Sven Schaust, Maximilian Walther, & Michael Kaisser. (2013). Avalanche: Prepare, manage, and understand crisis situations using social media analytics. In J. Geldermann and T. Müller S. Fortier F. F. T. Comes (Ed.), ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management (pp. 852–857). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: The recent rise of Social Media services has created huge streams of information which can be very valuable in a variety of scenarios. One specific scenario that has received interest is how Social Media analytics can be beneficial in crisis situations. In this paper, we describe our vision for a Social Media-ready command and control center. As motivation for our work, we present a short analysis of tweets issued in NYC during Hurricane Sandy in late October 2012 and we give an overview of the architecture of our event detection subsystem.
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Svend-Jonas Schelhorn, Benjamin Herfort, Richard Leiner, Alexander Zipf, & João Porto De Albuquerque. (2014). Identifying elements at risk from OpenStreetMap: The case of flooding. In and P.C. Shih. L. Plotnick M. S. P. S.R. Hiltz (Ed.), ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management (pp. 508–512). University Park, PA: The Pennsylvania State University.
Abstract: The identification of elements at risk is an essential part in hazard risk assessment. Especially for recurring natural hazards like floods, an updated database with information about elements exposed to such hazards is fundamental to support crisis preparedness and response activities. However, acquiring and maintaining an up-to-date database with elements at risk requires both detailed local and hazard-specific knowledge, being often a challenge for local communities and risk management bodies. We present a new approach for leveraging Volunteered Geographic Information to identify elements at risk from the free and open-source mapping project OpenStreetMap. We present initial results from a case study in the city of Cologne, Germany, to validate our approach in the case of flood-hazard. Our results show that the identification of elements at flood risk from OpenStreetMap is a suitable and cost-effective alternative for supporting local governments and communities in risk assessment and emergency planning.
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Kate Starbird, & Jeannie Stamberger. (2010). Tweak the tweet: Leveraging microblogging proliferation with a prescriptive syntax to support citizen reporting. In C. Zobel B. T. S. French (Ed.), ISCRAM 2010 – 7th International Conference on Information Systems for Crisis Response and Management: Defining Crisis Management 3.0, Proceedings. Seattle, WA: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: In this paper, we propose a low-tech solution for use by microbloggers that could enhance their ability to rapidly produce parsable, crisis-relevant information in mass emergencies. We build upon existing research on the use of social media during mass emergencies and disasters. Our proposed intervention aims to leverage the affordances of mobile microblogging and the drive to support citizen reporting within current behavioral Twitter-based microblogging practice. We introduce a prescriptive, tweet-based syntax that could increase the utility of information generated during emergencies by gently reshaping current behavioral practice. This offering is grounded in an understanding of current trends in norm evolution of Twitter use, an evolution that has progressed quickly but appears to be stabilizing around specific textual conventions.
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Sardar Muhammad Sulaman, Taimor Abbas, Krzysztof Wnuk, & Martin Höst. (2014). Hazard analysis of collision avoidance system using STPA. In and P.C. Shih. L. Plotnick M. S. P. S.R. Hiltz (Ed.), ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management (pp. 424–428). University Park, PA: The Pennsylvania State University.
Abstract: As our society becomes more and more dependent on IT systems, failures of these systems can harm more and more people and organizations both public and private. Diligently performing risk and hazard analysis helps to minimize the societal harms of IT system failures. In this paper we present experiences gained by applying the System Theoretic Process Analysis (STPA) method for hazard analysis on a forward collision avoidance system. Our main objectives are to investigate effectiveness in terms of the number and quality of identified hazards, and time efficiency in terms of required efforts of the studied method. Based on the findings of this study STPA has proved to be an effective and efficient hazard analysis method for assessing the safety of a safety-critical system and it requires a moderate level of effort.
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Teun Terpstra, Richard Stronkman, Arnout De Vries, & Geerte L. Paradies. (2012). Towards a realtime Twitter analysis during crises for operational crisis management. 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: Today's crises attract great attention on social media, from local and distant citizens as well as from news media. This study investigates the possibilities of real-time and automated analysis of Twitter messages during crises. The analysis was performed through application of an information extraction tool to nearly 97,000 tweets that were published shortly before, during and after a storm hit the Pukkelpop 2011 festival in Belgium. As soon as the storm hit the festival tweet activity increased exponentially, peaking at 576 tweets per minute. The extraction tool enabled analyzing tweets through predefined (geo)graphical displays, message content filters (damage, casualties) and tweet type filters (e.g., retweets). Important topics that emerged were 'early warning tweets', 'rumors' and the 'self-organization of disaster relief' on Twitter. Results indicate that automated filtering of information provides valuable information for operational response and crisis communication. Steps for further research are discussed. © 2012 ISCRAM.
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Tina Mioch, Reinier Sterkenburg, Tatjana Beuker, & Mark A. Neerincx. (2021). Actionable Situation Awareness: Supporting Team Decisions in Hazardous Situations. 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. 62–70). Blacksburg, VA (USA): Virginia Tech.
Abstract: Situation Awareness (SA) has been recognized and studied as an important requirement for an effective task performance of first responders. The integration of increasingly advanced sensor, network and artificial intelligence technology into the work processes affects the building, maintenance and sharing of SA. Connecting SA to decision support models provides new possibilities for the development of actionable SA (aSA), entailing information that guides the momentary decision-making processes of the concerning actors. In the European ASSISTANCE project, we are developing an aSA module that displays information about gas distributions, its current and predicted future states (e.g., entailing risks of breathing-in of toxic gases), with references to effective decision-making patterns for this situation. The aSA model is continuously updated based on sensor data. This paper gives an overview of this aSA module for chemical hazard prediction and corresponding display, and presents initial team design patterns that will be integrated into this display to support its actionability.
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David J. Wald. (2013). Adding secondary hazard and ground-truth observations to PAGER's loss modeling. In J. Geldermann and T. Müller S. Fortier F. F. T. Comes (Ed.), ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management (pp. 586–591). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: A rapid, holistic view of earthquake disasters begins with earthquake location and magnitude, alerted by seismic networks. The initial source characteristics, along with any available ground-shaking observations, can be used to rapidly estimate the shaking extent, its severity (e.g., ShakeMap), and its likely impact to society, for example, employing the U.S. Geological Survey's Prompt Assessment of Global Earthquakes for Response, or PAGER, system. When serous impacts are likely, PAGER's impact-based alerts can, in turn, begin the process of primary response at the local, national, or international level, and the process of reconnaissance via social media, the mainstream media, scientific analyses, and remotely-sensed and ground-truth observations. In this work-in-progress report, we describe our initial efforts to incorporate event-specific ground-truth observations and model secondary ground-failure hazards back into the loss-modeling domain in order to provide a more holistic view each earthquake disaster.
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Jian Wang, Daniela Rosca, Williams Tepfenhart, & Allen Milewski. (2006). Incident command system workflow modeling and analysis: A case study. In M. T. B. Van de Walle (Ed.), Proceedings of ISCRAM 2006 – 3rd International Conference on Information Systems for Crisis Response and Management (pp. 127–136). Newark, NJ: Royal Flemish Academy of Belgium.
Abstract: The dynamics and volunteer-based workforce characteristics of incident command systems have raised significant challenges to workflow management systems. Incident command systems must be able to adapt to ever changing surroundings and tasks during an incident. These changes need to be known by all responsible parties, since people work in shifts, get tired or sick during the management of an incident. In order to create this awareness, job action sheets and forms have been created. We propose a paperless system that can dynamically take care of these aspects, and formally verify the correctness of the workflows. Furthermore, during an incident, the majority of workers are volunteers that vary in their knowledge of computers, or workflows. To address these challenges, we developed an intuitive, yet formal approach to workflow modeling, modification, enactment and validation. In this paper, we show how to apply this approach to address the needs of a typical incident command system workflow.
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Jean-Luc Wybo. (2006). Improving resilience of organizations by increasing mutual knowledge of stakeholders. In M. T. B. Van de Walle (Ed.), Proceedings of ISCRAM 2006 – 3rd International Conference on Information Systems for Crisis Response and Management (pp. 540–546). Newark, NJ: Royal Flemish Academy of Belgium.
Abstract: Managing accidents and crisis is a complex task, which is achieved by a large number of stakeholders. In order to identify appropriate responses to risk-prone situations, a classification in two categories has been proposed: risks of damage and risks of crisis (Wybo 2004). Risks of damage correspond to emergency management procedures and plans. Risks of crisis correspond to situations that escape from planning because of the overflow of the organization. Resilience of organizations is defined as their ability to resist to chaos and to maintain the situation under control. From the analysis of a large number of emergencies and crises caused by industrial and natural hazards, we try to identify what conditions increase the resilience of organizations. They have in common to increase the mutual knowledge of stakeholders about their strategies and roles and about the development of the situation.
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Yajie Li, Amanda Lee Hughes, & Peter D. Howe. (2018). Communicating Crisis with Persuasion: Examining Official Twitter Messages on Heat Hazards. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 469–479). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Official crisis messages need to be persuasive to promote appropriate public responses. However, little research has examined the content of crisis messages from a persuasion perspective, especially for natural hazards. This study deductively identifies five persuasive message factors (PMFs) applicable to natural hazards, including two under-examined health-related PMFs: health risk susceptibility and health impact. Using 2016 heat hazards as a case study, this paper content-analyzes heat-related Twitter messages (N=904) posted by eighteen U.S. National Weather Service Weather Forecast Offices according to the five PMFs. We find that the use of descriptions of hazard intensity is disproportionately high, with a lack of use of other PMFs. We also describe different types of statements used to signal the two health-related PMFs. We conclude with implications and recommendations relevant to practitioners and researchers in social media crisis communication.
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Yan Wang, Hong Huang, Lida Huang, Minyan Han, Yiwu Qian, & Boni Su. (2017). An Agile Framework for Detecting and Quantifying Hazardous Gas Releases. 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. 42–49). Albi, France: Iscram.
Abstract: In response to the threat of hazardous gas releases to public safety and health, we propose an agile framework for detecting and quantifying gas emission sources. Emerging techniques like high-precision gas sensors, source term estimation algorithms and Unmanned Aerial Vehicles are incorporated. The framework takes advantage of both stationary sensor network method and mobile sensing approach for the detection and quantification of hazardous gases from fugitive, accidental or deliberate releases. Preliminary results on street-level detection of urban natural gas leakage is presented. Source term estimation is demonstrated through a synthetic test case, and is verified using Cramér-Rao bound analysis.
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Yan Wang, Hong Huang, & Wei Zhu. (2015). Stochastic source term estimation of HAZMAT releases: algorithms and uncertainty. In L. Palen, M. Buscher, T. Comes, & A. Hughes (Eds.), ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management. Kristiansand, Norway: University of Agder (UiA).
Abstract: Source term estimation (STE) of hazardous material (HAZMAT) releases is critical for emergency response. Such problem is usually solved with the aid of atmospheric dispersion modelling and inversion algorithms accompanied with a variety of uncertainty, including uncertainty in atmospheric dispersion models, uncertainty in meteorological data, uncertainty in measurement process and uncertainty in inversion algorithms. Bayesian inference methods provide a unified framework for solving STE problem and quantifying the uncertainty at the same time. In this paper, three stochastic methods for STE, namely Markov chain Monte Carlo (MCMC), sequential Monte Carlo (SMC) and ensemble Kalman filter (EnKF), are compared in accuracy, time consumption as well as the quantification of uncertainty, based on which a kind of flip ambiguity phenomenon caused by various uncertainty in STE problems is pointed out. The advantage of non-Gaussian estimation methods like SMC is emphasized.
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Zachary Sutherby, & Brian Tomaszewski. (2018). Conceptualizing the Role Geographic Information Capacity has on Quantifying Ecosystem Services under the Framework of Ecological Disaster Risk Reduction (EcoDRR). In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 326–333). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: The use of ecosystems for EcoDRR is a beneficial and a viable option for community stakeholders. For example, ecosystems can mitigate the effects of hazards experienced in anthropogenic communities. Ecosystem services are the underlying reason for this benefit. EcoDRR is the idea of sustainable management, conservation, and restoration of ecosystems to maximize ecosystem services and reduce disaster risks and impacts. The use of geospatial technologies to monitor large-scale ecosystems are often subject to Geographic Information Capacity (GIC), or the ability of ecosystem stakeholders to utilize all existing geographic information, resources, and capacities to monitor ecosystem services. Though these tools are useful, currently there is not a tool that specifically quantifies ecosystem services in the context of DRR. The main contribution of this paper is a conceptual framework intended to quantify ecosystem services in the context of EcoDRR.
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