Abstract: Because of the apparent ineffectiveness of current disaster plans, we focus our research on modeling emergency response activities. If we can capture the crucial concepts of emergency response in a mathematical framework and apply this framework to construct disaster plans, then we pave the way for the development of automated decisions support systems for emergency response.

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.

Abstract: When a disaster occurs it is critical that emergency response information systems share a common ontology to support their disaster management alerting functions and notifications. Notifications are critical when an emergency scenario is going to happen (e.g. a typhoon approaching) so it is crucial, for emergency systems, to be able to transmit them to all kinds of recipients. An ontology was developed by investigating different sources: accessibility guidelines, emergency response systems, communication devices and technologies, taking into account the different abilities of people to react to different alarms (e.g. mobile phone vibration as an alarm for deaf people). We think that the proposed ontology addresses the information needs for sharing and integrating emergency notification messages and contents over different emergency response information systems and to be accessible under different conditions and for different kind of users.

Abstract: The paper addresses the issue of context-aware operational decision support in emergency situations. A decision support system (DSS) developed for this purpose is implemented as a network of a set of Web-services. The Web-services try to organise a service network according to context. Here the context is proposed to be modelled as a “problem model”. It specifies problems to be solved in a particular kind of emergency situation. Context is produced based on the knowledge extracted from the application domain (application ontology) and formalised by a set of constraints. The purpose of the service network is provision the DSS with contextualised information from diverse information sources and solving problems specified in the context. In the framework of context-aware operational decision support, composition of the application ontology for the disaster management domain from the Semantic Web Ontologies is discussed and a hybrid technology of context-aware operational decision support is presented. The technology is based on ontology management, context management, constraint satisfaction, and Web Services. Application of the ideas above is illustrated by an example of a decision support system for real-time resource coordination and situation awareness for logistics management in fire response operations.

Abstract: The goal of the paper is to discuss the framework for an interdisciplinary human-computer interactive technology that facilitates information and resource exchange and forms core groups for crisis management. The social networks discussed here are designed to incorporate local knowledge and participation and to foster institutional and academic ties by modeling interrelationships among global communities and exploring policy options. Social interactions between individuals and organizations are explored especially in situations when directed responses are helpful in predicting the complex interplay between social, political, and technological systems and practices that result in a transfer of information and resources in disaster situations. In the future, such networks shall identify patterns through which groups interact in responding to critical issues and shall incorporate more complicated actions by individuals and organizations allowing them to move away from a rigid path to manage disasters via the most situationally appropriate routes.

Abstract: This review addresses current educational and research efforts in disaster medical education (DME) in the United States. Since the events of 9/11, DME has received greater attention. However substantial problems remain in terms of ensuring that large numbers of medical students and residents are exposed to high quality DME – not only Emergency Medicine residents. Barriers to widespread adoption of DME include lack of performance metrics, disagreement task areas, and lack of emphasis on physician leadership. Further, such efforts must ensure retention of key information over periods that are disaster free; utilize objective training metrics that will allow for an evidence base to form; and develop low cost, scalable training approaches that offer greater fidelity to the disaster environment than classroom based instruction. To improve the state of the art, we argue that DME research must move toward a translational science model that integrates important advances in basic information science into application that improve the clinical performance of frontline medical staff who are called on to respond to individual and community needs in the aftermath of disaster. Mid-fidelity, team-in-the-loop simulations developed for disaster manager training may provide an avenue toward improved DME by exposing medical students to scenarios that fundamentally challenge their assumptions in real-time game play. This can be accomplished with lower costs and greater scalability than live exercise or mock-up training approaches. © 2012 ISCRAM.

Abstract: This paper aims to lead a discourse on distributing mobile applications while having no access to cloud services. While in daily life people acquire applications via application stores, the access to those stores can be severely hampered in disasters. Instead of wishful thinking, i.e. hoping that people or manufactures would pre-install an (emergency) app before a disaster, we have started to investigate into Internet-less deployment mechanisms. We discuss five potential approaches of deploying apps in an ad-hoc fashion. Each approach is assessed against six criteria, while taking a stance that the smartphone is the minimally required deployment platform. This paper concludes with the observation that current mobile operating system providers do not provide “Internet-less” deployment mechanisms, although platforms as Android allow for this. This said, we hope that this contribution can spark further interest into the discussed problematic.

Abstract: Responding to a large-scale disaster such as an earthquake or hurricane is a collective problem. Human agents are increasingly collaborating with non-human agents (autonomous systems) in attempt to respond to a disaster. IntCris is a prototype intended to bring together interaction for human and non-human agents to aid the decision-making process by focusing on how to facilitate the “correct information to the correct agent” problem as well as encouraging new and agile behaviour. We focus on three categories of information: command, report and personal with a formal grammar to accompany the implementation. The requirements for the software were inspired by real life case studies from Hurricane Katrina, the Fukoshima Nuclear Disaster and Hurricane Sandy. The contribution of this work is to advance technology that brings together HAS (human and autonomous system interaction), in addition to enhancing collective intelligence.

Abstract: The most popular micro blogging platform Twitter has been the topic of a variety of research papers related to disaster and crisis management. As an essential first step and basis for a real-time methodology to exploit Twitter for event detection, localization and ultimately semantic content analysis, a functional model to describe the amount of tweets during a day has been developed. It was derived from a corpus of messages in an exemplary area of investigation. To satisfy the different daily behavior on particular days, two types of days are distinguished in this paper. Moreover, keyword-adjusted data is used to point out the potential of semantic tweet analysis in following steps. The consideration of spatial event descriptions in relevant tweets could significantly improve and accelerate the perception of a disaster. The results from the conducted tests demonstrate the capability of the functional model to detect events with significant social impact in Twitter data.

Abstract: The widespread availability of network-enabled handled devices has made the development of pervasive computing applications an emerging reality particularly suitable for managing emergency/disaster situations. Moreover in emergency management scenarios, Geographic Information Systems (GIS) are gaining momentum for their capacity to capture, analyze and manage geo-referenced data. In this paper we discuss an architecture designed to support rescue teams operating in outdoor environments and equipped with mobile devices working in a P2P fashion within a Mobile Ad-hoc Network (MANET). Our system has been designed to effectively address the on-field working persons' need for geographic information that cannot be supplied by conventional paper-based maps. Our approach provides a transparent access to geo-information and to GIS functionalities, and it addresses issues specifically relevant to emergency management scenarios in open fields.

Abstract: In this paper we present findings from interviews conducted with representatives from large international disaster response organizations concerning their use of social media data in crisis response. We present findings in which the barriers to use by responding organizations have gone beyond simple discussions of trustworthiness to that of more operational issues rather than mere data quality. We argue that the landscape of the use of microblogged data in crisis response is varied, with pockets of use and acceptance among organizations. We found that microblogged data is useful to responders in situations where information is limited, such as at the beginning of an emergency response effort, and when the risks of ignoring an accurate response outweigh the risks of acting on an incorrect one. In some situations, such as search and rescue operations, microblogged data may never meet the standards of quality required. In others, such as resource and supply management, microblogging data could be useful as long as it is appropriately verified and classified.

Abstract: Social media are increasingly becoming a source for event-based early warning systems in the sense that they can help to detect natural disasters and support crisis management during or after disasters. In this work-in-progress paper we study the problems of analyzing multilingual twitter feeds for emergency events. The present work focuses on English as “lingua franca” and on under-resourced Mediterranean languages in endangered zones, particularly Turkey, Greece, and Romania Generally, as local civil protection authorities and the population are likely to respond in their native language. We investigated ten earthquake events and defined four language-specific classifiers that can be used to detect earthquakes by filtering out irrelevant messages that do not relate to the event. The final goal is to extend this work to more Mediterranean languages and to classify and extract relevant information from tweets, translating the main keywords into English. Preliminary results indicate that such a filter has the potential to confirm forecast parameters of tsunami affecting coastal areas where no tide gauges exist and could be integrated into seismographic sensor networks. © 2012 ISCRAM.