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	Author	Alexander Gabriel; Babette Tecklenburg; Yann Guillouet; Frank Sill Torres
	Title	Threat analysis of offshore wind farms by Bayesian networks – a new modeling approach			Type	Conference Article
	Year	2021	Publication	ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2021
	Volume		Issue		Pages	174-185
	Keywords	Threat analysis, Bayesian networks, process modeling, Critical infrastructurs
	Abstract	As a result of the ongoing commitment to climate protection in more and more countries and the corresponding expansion of renewable energies, the importance of renewables for the security of electricity supply is also increasing. Wind energy generated in offshore wind farms already accounts for a significant share of the energy mix and will continue to grow in the future. Therefore, approaches and models for security assessment and protection against threats are also needed for these infrastructures. Due to the special characteristics and geographical location of offshore wind farms, they are confronted with particular challenges. In this context, this contribution outlines how an approach for threat analysis of offshore wind farms is to be developed within the framework of the new research project “ARROWS” of the German Aerospace Center. The authors first explain the structure of offshore wind farms and then present a possible modeling approach using Qualitative function models and Bayesian networks.
	Address	German Aerospace Center – Institute for the Protection of Maritime Infrastructures; German Aerospace Center – Institute for the Protection of Maritime Infrastructures; German Aerospace Center – Institute for the Protection of Maritime Infrastructures; Ger
	Corporate Author				Thesis
	Publisher	Virginia Tech	Place of Publication	Blacksburg, VA (USA)	Editor	Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	978-1-949373-61-5	ISBN		Medium
	Track	Analytical Modeling and Simulation	Expedition		Conference	18th International Conference on Information Systems for Crisis Response and Management
	Notes	Alexander.Gabriel@dlr.de			Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2323
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	Author	Dragos Datcu; Leon J.M. Rothkrantz
	Title	A Dialog Action Manager for automatic crisis management			Type	Conference Article
	Year	2008	Publication	Proceedings of ISCRAM 2008 – 5th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	ISCRAM 2008
	Volume		Issue		Pages	384-393
	Keywords	Bayesian networks; Information systems; Management information systems; Mobile devices; Safe handling; Automatic systems; Crisis management; Crisis management systems; Crisis situations; Data-communication; Reasoning; System architectures; System knowledge; Managers
	Abstract	This paper presents the results of our research on the development of a Dialog Action Manager-DAM as part of a complex crisis management system. Imagine the utility of such an automatic system to detect the crisis and to provide support to people in contexts similar to what happened recently at the underground in London and Madrid. Preventing and handling the scenarios of terrorism and other crisis are nowadays maybe the most important issues for a modern and safe society. In order to automate the crisis support, DAM simulates the behavior of an employee at the crisis centre handling telephone calls from human observers. Firstly, the system has to mimic the natural support for the paradigm 'do you hear me?' and next for the paradigm 'do you understand me?'. The people witnessing the crisis event as well as human experts provide reports and expertise according to their observations and knowledge on the crisis. The system knowledge and the data communication follow the XML format specifications. The system is centered on the results of our previous work on creating a user-centered multimodal reporting tool that works on mobile devices. In our paper we describe the mechanisms for creating an automatic DAM system that is able to analyze the user messages, to identify and track the crisis contexts, to support dialogs for crisis information disambiguation and to generate feedback in the form of advice to the users. The reasoning is done by using a data frame and rule based system architecture and an alternative Bayesian Network approach. In the paper we also present a series of experiments we have attempted in our endeavor to correctly identify natural solutions for the crisis situations.
	Address	Man-Machine Interaction Group, Delft University of Technology, 2628 CD, Delft, Netherlands
	Corporate Author				Thesis
	Publisher	Information Systems for Crisis Response and Management, ISCRAM	Place of Publication	Washington, DC	Editor	F. Fiedrich, B. Van de Walle
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	9780615206974	Medium
	Track	Observation Systems in Crisis Situations	Expedition		Conference	5th International ISCRAM Conference on Information Systems for Crisis Response and Management
	Notes				Approved	no
	Call Number				Serial	424
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	Author	Olof Görnerup; Per Kreuger; Daniel Gillblad
	Title	Autonomous accident monitoring using cellular network data			Type	Conference Article
	Year	2013	Publication	ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	ISCRAM 2013
	Volume		Issue		Pages	638-646
	Keywords	Bayesian networks; Carrier mobility; Inference engines; Information systems; Sensor networks; Traffic congestion; Anomaly detection; Bayesian inference; Cellular network; Crisis management; Emergency response; Large scale sensor network; Mobile communication networks; Vehicular traffic scenarios; Accidents
	Abstract	Mobile communication networks constitute large-scale sensor networks that generate huge amounts of data that can be refined into collective mobility patterns. In this paper we propose a method for using these patterns to autonomously monitor and detect accidents and other critical events. The approach is to identify a measure that is approximately time-invariant on short time-scales under regular conditions, estimate the short and long-term dynamics of this measure using Bayesian inference, and identify sudden shifts in mobility patterns by monitoring the divergence between the short and long-term estimates. By estimating long-term dynamics, the method is also able to adapt to long-term trends in data. As a proof-of-concept, we apply this approach in a vehicular traffic scenario, where we demonstrate that the method can detect traffic accidents and distinguish these from regular events, such as traffic congestions.
	Address	Swedish Institute of Computer Science, Sweden
	Corporate Author				Thesis
	Publisher	Karlsruher Institut fur Technologie	Place of Publication	KIT; Baden-Baden	Editor	T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	9783923704804	Medium
	Track	Analytical Modelling and Simulation	Expedition		Conference	10th International ISCRAM Conference on Information Systems for Crisis Response and Management
	Notes				Approved	no
	Call Number				Serial	537
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	Author	Aibek Musaev; De Wang; Calton Pu
	Title	LITMUS: Landslide detection by integrating multiple sources			Type	Conference Article
	Year	2014	Publication	ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	ISCRAM 2014
	Volume		Issue		Pages	677-686
	Keywords	Bayesian networks; Disasters; Hazards; Information systems; Integration; Landslides; Nasa; Rain; Rain gages; Landslide detection; Litmus; Multi-source integrations; Physical sensors; Social sensors; Data integration
	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.
	Address	Georgia Institute of Technology, United States
	Corporate Author				Thesis
	Publisher	The Pennsylvania State University	Place of Publication	University Park, PA	Editor	S.R. Hiltz, M.S. Pfaff, L. Plotnick, and P.C. Shih.
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	9780692211946	Medium
	Track	Social Media in Crisis Response and Management	Expedition		Conference	11th International ISCRAM Conference on Information Systems for Crisis Response and Management
	Notes				Approved	no
	Call Number				Serial	801
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	Author	Ahmed Nagy; Jeannie Stamberger
	Title	Crowd sentiment detection during disasters and crises			Type	Conference Article
	Year	2012	Publication	ISCRAM 2012 Conference Proceedings – 9th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	ISCRAM 2012
	Volume		Issue		Pages
	Keywords	Bayesian networks; Emergency services; Information systems; Risk management; Social networking (online); Crisis management; Disaster response; Emergency management; Short message; Twitter; Disasters
	Abstract	Microblogs are an opportunity for scavenging critical information such as sentiments. This information can be used to detect rapidly the sentiment of the crowd towards crises or disasters. It can be used as an effective tool to inform humanitarian efforts, and improve the ways in which informative messages are crafted for the crowd regarding an event. Unique characteristics of microblogs (lack of context, use of jargon etc) in Tweets expressed by a message-sharing social network during a disaster response require special handling to identify sentiment. We present a systematic evaluation of approaches to accurately and precisely identify sentiment in these Tweets. This paper describes sentiment detection expressed in 3698 Tweets, collected during the September 2010, San Bruno, California gas explosion and resulting fires. The data collected was manually coded to benchmark our techniques. We start by using a library of words with annotated sentiment, SentiWordNet 3.0, to detect the basic sentiment of each Tweet. We complemented that technique by adding a comprehensive list of emoticons, a sentiment based dictionary and a list of out-of-vocabulary words that are popular in brief, online text communications such as lol, wow, etc. Our technique performed 27% better than Bayesian Networks alone, and the combination of Bayesian networks with annotated lists provided marginal improvements in sentiment detection than various combinations of lists. Â© 2012 ISCRAM.
	Address	Carnegie Mellon Silicon Valley, IMT Lucca Institute of Advanced Studies, United States; Disaster Management Initiative, Carnegie Mellon Silicon Valley, United States
	Corporate Author				Thesis
	Publisher	Simon Fraser University	Place of Publication	Vancouver, BC	Editor	L. Rothkrantz, J. Ristvej, Z.Franco
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	9780864913326	Medium
	Track	Social Media and Collaborative Systems	Expedition		Conference	9th International ISCRAM Conference on Information Systems for Crisis Response and Management
	Notes				Approved	no
	Call Number				Serial	173
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	Author	Leon J.M. Rothkrantz; Zhenke Yang
	Title	Crowd control by multiple cameras			Type	Conference Article
	Year	2009	Publication	ISCRAM 2009 – 6th International Conference on Information Systems for Crisis Response and Management: Boundary Spanning Initiatives and New Perspectives	Abbreviated Journal	ISCRAM 2009
	Volume		Issue		Pages
	Keywords	Bayesian networks; Computer vision; Bayesian reasoning; Conditional probabilities; Crisis events; Crowd control; Delft University of Technology; Multiple cameras; Scenarios; Scripts; Information systems
	Abstract	One of the goals of the crowd control project at Delft University of Technology is to detect and track people during a crisis event, classify their behavior and assess what is happening. The assumption is that the crisis area is observed by multiple cameras (fixed or mobile). The cameras sense the environment and extract features such as the amount of motion. These features are the input to a Bayesian network with nodes corresponding to situations such as terroristic attack, fire, and explosion. Given the probabilities of the observed features, by reasoning, the likelihood of the possible situations can be computed. A prototype was tested in a train compartment and its environment. Forty scenarios, performed by actors, were recorded. From the recordings the conditional probabilities have been computed. The scenarios are designed as scripts which proved to be a good methodology. The models, experiments and results will be presented in the paper.
	Address	Man-Machine Interaction Group, Delft University of Technology, 2628CD Delft, Netherlands; SEWACO Faculty of Military Sciences, Netherlands Defence Academy, Den Helder, Netherlands
	Corporate Author				Thesis
	Publisher	Information Systems for Crisis Response and Management, ISCRAM	Place of Publication	Gothenburg	Editor	J. Landgren, S. Jul
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	9789163347153	Medium
	Track	Open Track	Expedition		Conference	6th International ISCRAM Conference on Information Systems for Crisis Response and Management
	Notes				Approved	no
	Call Number				Serial	894
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	Author	Don J.M. Willems; Louis Vuurpijl
	Title	Designing interactive maps for crisis management			Type	Conference Article
	Year	2007	Publication	Intelligent Human Computer Systems for Crisis Response and Management, ISCRAM 2007 Academic Proceedings Papers	Abbreviated Journal	ISCRAM 2007
	Volume		Issue		Pages	159-166
	Keywords	Bayesian networks; Feature extraction; Human computer interaction; Personal computers; Crisis management; Crisis management systems; Data collection; Domain specific; Effective communication; Interactive maps; Mode detection; Recognition systems; Pattern recognition systems
	Abstract	This paper describes the design, implementation, and evaluation of pen input recognition systems that are suited for so-called interactive maps. Such systems provide the possibility to enter handwriting, drawings, sketches and other modes of pen input. Typically, interactive maps are used to annotate objects or mark situations that are depicted on the display of video walls, handhelds, PDAs, or tablet PCs. Our research explores the possibility of employing interactive maps for crisis management systems, which require robust and effective communication of, e.g., the location of objects, the kind of incidents, or the indication of route alternatives. The design process described here is a mix of “best practices” for building perceptive systems, combining research in pattern recognition, human factors, and human-computer interaction. Using this approach, comprising data collection and annotation, feature extraction, and the design of domain-specific recognition technology, a decrease in error rates is achieved from 9.3% to 4.0%.
	Address	Nijmegen Institute for Cognition and Information, Radboud University, Nijmegen, Netherlands
	Corporate Author				Thesis
	Publisher	Information Systems for Crisis Response and Management, ISCRAM	Place of Publication	Delft	Editor	B. Van de Walle, P. Burghardt, K. Nieuwenhuis
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	9789054874171; 9789090218717	Medium
	Track	HCIS	Expedition		Conference	4th International ISCRAM Conference on Information Systems for Crisis Response and Management
	Notes				Approved	no
	Call Number				Serial	1092
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