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Thomas Bernard, Mathias Braun, Olivier Piller, Denis Gilbert, Jochen Deuerlein, Andreas Korth, et al. (2013). SMaRT-OnlineWDN: Online security management and reliability toolkit for water distribution networks. 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. 171–176). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: Water distribution Networks (WDNs) are critical infrastructures that are exposed to deliberate or accidental contamination. Until now, no monitoring system is capable of protecting a WDN in real time. In the immediate future water service utilities that are installing water quantity and quality sensors in their networks will be producing a continuous and huge data stream for treating. The main objective of the project SMaRT-OnlineWDN is the development of an online security management toolkit for water distribution networks that is based on sensor measurements of water quality as well as water quantity and online simulation. Its field of application ranges from detection of deliberate contamination, including source identification and decision support for effective countermeasures, to improved operation and control of a WDN under normal and abnormal conditions.
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Han Che, & Shuming Liu. (2013). Monitoring data identification for a water distribution system based on data self-recognition approach. 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. 166–170). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: Detecting the occurrence of hydraulic accidents or contamination events in the shortest time has always been a significant but difficult task. The simple and efficient way is to identify the sudden changes or outliers hidden in the vast amounts of monitoring data produced minute by minute, which is unpractical for human. A new method, which employs a data self-recognition approach to achieve that automatically, has been proposed in this paper. The autoregressive moving average (ARMA) model was employed in this research to construct the self-recognition model. 56 months monitoring data from Changping water distribution network in Beijing, which was firstly cut into different time-slice series, was used to establish the ARMA model. This provided a prediction confidence interval in order to identify the outliers in the test data series. The results showed a good performance in outlier identification and the accuracy ranges from 90% to 95%.Thus, the ARMA model showed great potential in dealing with monitoring data and achieving the expected performance of data self-recognition technology.
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Daniel Hahn. (2007). Non-restrictive linking in wireless sensor networks for industrial risk management. 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. 605–609). Delft: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: The OSIRIS project addresses the disaster management workflow in the phases of risk monitoring and crisis management. Risk monitoring allows the continuous observation of endangered areas combined with sensor deployment strategies. The crisis management focuses on particular events and the support by sensor networks. Four complementary live demonstrations will validate the OSIRIS approach. These demonstrations include water contamination, air pollution, south European forest fire, and industrial risk monitoring. This paper focuses on the latter scenario: the industrial risk monitoring. This scenario offers the special opportunity to demonstrate the relevance of OSIRIS by covering all the aspects of monitoring, preparation and response phases of both environmental risk and crisis management. The approach focuses on non-restrictive linking in a wireless sensor network in order to facilitate the addition and removal of nodes providing open interaction primitives allowing the comfortable integration, exclusion, and modification. A management layer with an event-triggered and service-based middleware is proposed. A live lab with real fire is illustrated.
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Magnus Jändel, Sinna Lindquist, & Linus Luotsinen. (2013). Social coverage maps. 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. 241–250). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: This paper introduces Social Coverage Maps (SCM) as a visual representation of the societal impact of localized disruptions in urban areas. Incited by the recent deliberate interruption of wireless services for the purpose of crowd control in San Francisco, we focus on the use of SCMs for representing emergent effects of electronic warfare. As a prequel we discuss maps and other visualizations as representations of human behaviour and relations. The SCM concept is defined and grounded in simulation-based parameters. Using an experimental scenario based on cell phone jamming in a city we show how SCMs are generated using an agent-based population simulator. We find that Social Coverage Maps could become a useful tool for analysing emergent effects of actions and events including electronic warfare, roadblocks, smoke, teargas, chemical and radioactive contamination with applications in operational and emergency planning as well as crisis management.
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Alexander Kiselev, & Sergey Bogatov. (2012). Model PROLOG for countermeasures efficacy assessment and its calculation algorithm verification on the base of the Chazhma Bay accident data. 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: Methodical approaches used in the computational model “PROLOG” are given in the paper. This model is intended for assessing radiological situations and an efficiency of counter measures after short term radioactive releases. Basic local Gaussian dispersion algorithm is supplemented with modules for assessing a plume rise, dry deposition velocities, effect of buildings and complex terrain, etc. The modules provide a compromise between simplicity, shortage of initial data and adequacy of the model in case of real accident. Approaches to assess the dose and countermeasure efficiency are presented as well. Plume rise, complex terrain and contaminant polydispersity modeling approaches were tested on the basis of comparison of calculation and experimental results for dose rate and Co-60 surface contamination measured after the Chazhma bay accident in 1985. © 2012 ISCRAM.
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