Ana Gabriela Núñez Avila, & Mª Carmen Penadés Gramage. (2019). Towards an organization certified in emergency plans 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: QuEP is a framework that guides organizations in assessing and improving their emergency plan management by
following a set of principles, practices, and techniques at the different maturity levels established in the QuEP model.
Its main objective is to be applied to real cases to discover the state of an organization?s emergency plan management
and recommend techniques for improvement. In this paper, we describe the first application of QuEP as a prior step
to its implementation and possible use in official certifications for emergency plans with a guarantee of quality. So,
we have applied a real case in a UPV building towards the certification of the emergency plan management.
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Mike Botts, George Percivall, Carl Reed, & John Davidson. (2008). OGC® sensor web enablement: Overview and high level architecture. In B. V. de W. F. Fiedrich (Ed.), Proceedings of ISCRAM 2008 – 5th International Conference on Information Systems for Crisis Response and Management (pp. 713–723). Washington, DC: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: A precursor paper (also available as an OGC White Paper) provides a high-level overview of and architecture for the Open Geospatial Consortium (OGC) standards activities that focus on sensors, sensor networks, and a concept called the “Sensor Web”. This OGC focus area is known as Sensor Web Enablement (SWE). For readers interested in greater technical and architecture details, please download and read the OGC SWE Architecture Discussion Paper titled “The OGC Sensor Web Enablement Architecture” (OGC document 06-021r1).
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Dragos Datcu, & Leon J.M. Rothkrantz. (2007). The use of active appearance model for facial expression recognition in crisis environments. In K. Nieuwenhuis P. B. B. Van de Walle (Ed.), Intelligent Human Computer Systems for Crisis Response and Management, ISCRAM 2007 Academic Proceedings Papers (pp. 515–524). Delft: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: In the past a crisis event was notified by local witnesses that use to make phone calls to the special services. They reported by speech according to their observation on the crisis site. The recent improvements in the area of human computer interfaces make possible the development of context-aware systems for crisis management that support people in escaping a crisis even before external help is available at site. Apart from collecting the people's reports on the crisis, these systems are assumed to automatically extract useful clues during typical human computer interaction sessions. The novelty of the current research resides in the attempt to involve computer vision techniques for performing an automatic evaluation of facial expressions during human-computer interaction sessions with a crisis management system. The current paper details an approach for an automatic facial expression recognition module that may be included in crisis-oriented applications. The algorithm uses Active Appearance Model for facial shape extraction and SVM classifier for Action Units detection and facial expression recognition.
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Kostas Kolomvatsos, Kakia Panagidi, & Stathes Hadjiefthymiades. (2013). Optimal spatial partitioning for resource allocation. 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. 747–757). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: Spatial partitioning consists of the problem of finding the best segmentation of an area under specific conditions. The final goal is to identify parts of the area where a number of resources could be allocated. Such cases are common in disaster management scenarios. In this paper, we consider such a scenario and propose a methodology for the resource allocation for emergency response. We utilize an intelligent technique that is based on the Particle Swarm Optimization algorithm. We define the problem by giving specific formulations and describe the proposed algorithm. Moreover, we provide a method for separating the area into cells and describe a technique for calculating cell weights based on the underlying spatial data. Finally, we present a case study for allocating a number of ambulances and give numerical results concerning the run time and the total coverage of the examined area.
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Julio Camarero Puras, & Carlos A. Iglesias. (2009). Disasters2.0. Application of Web2.0 technologies in emergency situations. In S. J. J. Landgren (Ed.), ISCRAM 2009 – 6th International Conference on Information Systems for Crisis Response and Management: Boundary Spanning Initiatives and New Perspectives. Gothenburg: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: This article presents a social approach for disaster management, based on a social portal, so-called Disasters2.0, which provides facilities for integrating and sharing user generated information about disasters. The architecture of Disasters2.0 is designed following REST principles and integrates external mashups, such as Google Maps. This architecture has been consumed with different clients, including a mobile client, a multiagent system for assisting in the decentralized management of disasters, and an expert system for automatic assignment of resources to disasters. As a result, the platform allows seamless collaboration of humans and intelligent agents, and provides a novel web2.0 approach for multiagent and disaster management research.
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Mohamad Rukieh. (2007). The effects of lineaments and epicentres on risk reduction in arabian rift zone. In K. Nieuwenhuis P. B. B. Van de Walle (Ed.), Intelligent Human Computer Systems for Crisis Response and Management, ISCRAM 2007 Academic Proceedings Papers (pp. 227–234). Delft: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: This paper describes the relationship between lineaments, which determined on space images, and the epicenters and their effects on spatial planning for risk reduction. Several studies have shown that most of the epicenters occur along these lineaments or their zones, or in the block regions which are bordered by these lineaments, or where these lineaments and different tectonic deformation are intersected. This paper presents a case study on the Arabian Rift Zone which is based on the linkages among lineaments, faults, and earthquakes that occurred in the region during 1910-93. Also, this study will show that most of these earthquakes were occurred along the main and secondary rift faults or in their zone, including the faults found in sea that helped in determining the courses of these earthquakes in the sea bottom. This confirms the importance of remote sensing techniques for providing space images of different scales in seismic studies.
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Peter Serwylo, Paul Arbon, & Grace Rumantir. (2011). Predicting patient presentation rates at mass gatherings using machine learning. In E. Portela L. S. M.A. Santos (Ed.), 8th International Conference on Information Systems for Crisis Response and Management: From Early-Warning Systems to Preparedness and Training, ISCRAM 2011. Lisbon: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Mass gatherings have been defined as events where more than 1,000 people are present for a defined period of time. Such an event presents specific challenges with respect to medical care. First aid is provisioned on-site at most events in order to prevent undue strain on the local emergency services. In order to allocate enough resources to deal with the expected injuries, it is important to be able to accurately predict patient volumes. This study used machine learning techniques to identify which variables are the most important in predicting patient volumes at mass gatherings. Data from 201 mass gatherings across Australia was analysed, finding that event type is the most predictive variable, followed by the state or territory, heat index, humidity, whether it is bounded, and the time of day. Variables with little bearing on the outcome included the presence of alcohol, whether the event was indoors or outdoors, and whether it had one point of focus. The best predictive models produced acceptable predictions of the patient presentations 80% of the time, and this could be further improved using optimization techniques.
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Evan A. Sultanik, & Clayton Fink. (2012). Rapid geotagging and disambiguation of social media text via an indexed gazetteer. 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: Microblogging services like Twitter afford opportunities for real time determination of situation awareness during crises as people report, via their statuses, information about events on the ground. An important component of the information included in a tweet are mentions of place names that may be sites of damage, injuries, or relief efforts. Methods for extracting these place names and determining the actual location being referenced are an essential part of the suite of tools required for automated extraction of situation awareness from tweets. Extracting and disambiguating place name mentions from text have been areas of extensive research. Twitter, however, presents challenges given the 140 character restriction on status and the informal, abbreviated language that are a norm in this communication channel. Named entity recognizers, which are dependent on labeled training data, may not be useful in this medium for extracting location mentions because the typical training domains for these taggers are absent the noise found in Twitter statuses. Additionally, the contextual information that is necessary for disambiguating place names is not always present. In this paper, we demonstrate a new technique, RapidGeo, for extracting and disambiguating place names from a location specific Twitter feed using an unsupervised technique for tagging location mentions and relying on the known geographic context of the feed for disambiguation. Our location tagging technique performs much better than an off-the-shelf named entity recognizer and we achieve reasonable precision in disambiguating extracted place names. We argue that such fast, high precision, unsupervised approaches are needed when important, actionable information is required from noisy data sources such as Twitter. © 2012 ISCRAM.
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