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.

Abstract: Collapsed buildings are one of the most complex challenges for rescue forces. The large amount of incoming information in particular is a major challenge to handle for the involved forces. The Assessment of this information is necessary in order to be able to give reliable statements about the number of buried and missing victims. To improve the handling of the information, the authors have developed a method to measure information flow during rescue exercises after the collapse of a building. This method has been developed within VERVE, a research project subsidized by the German Federal Ministry of Education and Research (BMBF). The method has been evaluated and tested through two exercises.

Abstract: The role of communication and information provision in coping with natural and man-made disasters and emergency situations is becoming increasingly important. In this paper we present an integrated mobile information and communication system, MIKoBOS, for emergency response operations that enables reliable data communication within the emergency site as well as between the site and the headquarters. It provides the responsible personnel involved in the emergency operation at different levels with anytime-anywhere access to relevant information. Compared to traditional voice-dominated approaches, the proposed system can greatly improve the effectiveness and efficiency of communication and coordination during disaster relief operations. Promising experimental performance results are provided for use with a number of terrestrial and satellite networks.

Abstract: Pre-positioning of supplies is important to facilitate disaster relief operations, however it is only after a disaster event occurs that the effectiveness of the pre-positioning strategy can be properly assessed. With this in mind, this paper analyzes a risk-based pre-positioning algorithm, developed for the American Red Cross, in the context of its actual performance in the 2013 Colorado Front Range floods. The paper assesses the relative effectiveness of the pre-positioning approach with respect to historical asset placements, and it discusses changes to the model that are necessary to support such comparisons and allow for further model extensions.

Abstract: This paper details the creation of a massive (over 32,000 messages) artificially constructed 'Twitter' microblog stream for a regional emergency preparedness functional exercise. By combining microblog conversion, manual production, and a control set, we created a web-based information stream providing valid, misleading, and irrelevant information to public information officers (PIOs) representing hospitals, fire departments, the local Red Cross, and city and county government officials. Addressing the challenges in constructing this corpus constitutes an important step in providing experimental evidence that complements observational study, necessary for designing effective social media tools for the emergency response setting. Preliminary results in the context of an emergency preparedness exercise suggest how social media can participate in the work practice of a PIO concerning the assessment of the disaster and the dissemination of information within the emergency response organization and to the public.

Abstract: In the Wellington Region, many lifelines are at risk, because they are in vulnerable narrow corridors close to active faults. In an earthquake, it is expected that these lifelines will be significantly damaged and unusable for extended periods of time. Because of this risk, many studies have been conducted to investigate the resulting downtimes. These studies, despite their usefulness, do not incorporate or make significant assumptions about localised factors. This paper summarises a thesis that aimed to improve the current predictive models, by including these local, and contextual influences. Multiple stakeholders who manage and repair the lifelines were interviewed to identify these factors which were then included into one of the current predictive models, and the influence on repair times was recorded. It was discovered that localised impacts such as staff logistics, land sliding, the land gradient, interdependency, and access doubled previous predicted repair times.

Abstract: This paper reviews the Quake-Catcher Network (QCN), a distributed computing seismic network that uses lowcost USB accelerometers to record earthquakes, and discusses the potential to incorporate QCN stations with traditional seismic networks. These very dense urban networks could then be used to create a working earthquake early warning system, as has been shown by our preliminary tests of the QCN in Christchurch, New Zealand. Although we have not yet attempted to add traditional seismometers to the QCN or supplement existing seismic networks with QCN sensors, we suggest that to do so would not be difficult, due to the simple nature of our network.

Abstract: In this paper, we develop a model with neural networks to localize events using microblogging data. Localization is the task of finding the location of an event and can be done by discovering event signatures in microblogging data. We use the deep learning methodology of Bi-directional Long Short-Term Memory (Bi-LSTM) to learn event signatures. We propose a methodology for labeling the Twitter date for use in Bi-LSTM However, there might not be enough data available to train the Bi-LSTM and learn the event signatures. Hence, the data is augmented using generative adversarial networks (GAN). Finally, we combine event signatures at different temporal and spatial granularity to improve the accuracy of event localization. We use microblogging data collected from Twitter to evaluate our model and compare it with other baseline methods.

Abstract: Good support for information sharing and processing is essential for successful crisis management. A crisis manager handles information from many different sources and collaborates with many different actors. This study is therefore focusing on specifying some needs and requirements for information support systems for crisis management command and control centres. The study is based on case studies and interviews in Sweden with the aim to understand how information processing best can be supported from a crisis manager's perspective. The needs and requirements found in the study can be used in future system design or improvement.

Abstract: Social media can be a highly valuable source of information during disasters. A crisis' development over time is of particular interest here, as social media messages can convey unfolding events in near-real time. Previous approaches for the automatic detection of information in such messages have focused on a static analysis, not taking temporal changes and already-known information into account. In this paper, we present a novel method for detecting new topics in incoming Twitter messages (tweets) conditional upon previously found related tweets. We do this by first extracting latent representations of each tweet using pre-trained sentence embedding models. Then, Infinite Mixture modeling is used to dynamically cluster these embeddings anew with each incoming tweet. Once a cluster reaches a minimum number of members, it is considered to be a new topic. We validate our approach on the TREC Incident Streams 2019A data set.

Abstract: In this article, we briefly describe a crisis management case that has been chosen (in the European funded project PLAY) to test the federated-open-trusted platform for event-driven interaction between services. A description of such complex use case in natural language is obviously limited and should be completed with a formal description methodology to gather the necessary knowledge. Considering our technical requirements we suggest to combine the S-Cube approach with the model driven architecture approach to propose a complete and structured case study description framework. Then this article presents a nuclear crisis case modeled according to these guidelines.

Abstract: The paper presents a four-phase action research project that was (and still is) conducted at the department of Information Management Customer Support and Operations (IM\CS&O) of a large multi-national company. The department is in charge of ICT-service continuity and has to produce ICT recovery plans that are integrated with the organization's overall Business Continuity plan. Interviews, Group Support System (GSS) technologies as well as a risk survey have been used to gather information and identify risks and threats. A systematic quantitative classification, measuring the impact of loss of ICT services on the company's business processes in terms of cost and risk will allow in the near future to utilize an economic decision model to prioritize the core activities of training and implementation of a recovery disaster plan. The research has made clear to the involved protagonists the necessity to share information, to develop awareness, and to formulate a shared recovery disaster plan to ensure ICT/business continuity and/or recovery when ICT disruptions occurs. © Proceedings ISCRAM 2004.