Chanthujan Chandrakumar, Raj Prasanna, Max Stephens, Marion Lara Tan, Caroline Holden, Amal Punchihewa, et al. (2023). Algorithms for Detecting P-Waves and Earthquake Magnitude Estimation: Initial Literature Review Findings. In V. L. Thomas J. Huggins (Ed.), Proceedings of the ISCRAM Asia Pacific Conference 2022 (pp. 138–155). Palmerston North, New Zealand: Massey Unversity.
Abstract: Earthquake Early Warning System (EEWS) plays a major role during an earthquake in alerting the public and authorities to take appropriate safety measures during an earthquake. Generally, EEWSs use three types of algorithms to generate alerts during an earthquake; namely: source-based, ground motion or wavefield-based and on-site-based approaches. However, source-based algorithms are commonly used in most of EEWSs worldwide. A source-based EEWS uses a particular time frame of the P-wave of an earthquake to estimate the source parameters such as magnitude and the location of that earthquake with the support of P-wave detection and earthquake magnitude and location estimation algorithms. As the initial step of a research project which aims to explore the best use of P-waves to generate earthquake alerts, this Work in Progress paper (WiPe) presents the initial partial findings from an ongoing literature review on exploring the algorithms used for P-wave detection and earthquake magnitude estimation.
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Md Fitrat Hossain, Thomas Kissane, Priyanka Annapureddy, Wylie Frydrychowicz, Sheikh Iqbal Ahamed, Naveen Bansal, et al. (2020). Implementing Algorithmic Crisis Alerts in mHealth Systems for Veterans with PTSD. In Amanda Hughes, Fiona McNeill, & Christopher W. Zobel (Eds.), ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management (pp. 122–133). Blacksburg, VA (USA): Virginia Tech.
Abstract: This paper seeks to establish a machine learning driven method by which a military veteran with Post-Traumatic Stress Disorder (PTSD) is classified as being in a crisis situation or not, based upon a given set of criteria. Optimizing alerting decision rules is critical to ensure that veterans at highest risk for mental health crisis rapidly receive additional attention. Subject matter experts in our team (a psychologist, a medical anthropologist, and an expert veteran), defined acute crisis, early warning signs and long-term crisis from this dataset. First, we used a decision tree to find an early time point when the peer mentors (who are also veterans) need to observe the behavior of veterans to make a decision about conducting an intervention. Three different machine learning algorithms were used to predict long term crisis using acute crisis and early warning signs within the determined time point.
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Michael K. Lindell. (2011). Evacuation modelling: Algorithms, assumptions, and data. 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: Survey researchers need to, Find out what assumptions evacuation modelers are making and collect empirical data to replace incorrect assumptions;, Obtain data on the costs of evacuation to households, businesses, and local government; and, Extend their analyses to address the logistics of evacuation and the process of re-entry. Evacuation modelers need to, Incorporate available empirical data on household evacuation behavior, and, Generate estimates of the uncertainties in their analyses. Cognitive scientists need to, Conduct experiments on hurricane tracking and evacuation decision making to better understand these processes, and, Develop training programs, information displays, and performance aids to assist local officials who have little or no previous experience in hurricane evacuation decision making.
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Konstantinos Koufos, Krisztina Cziner, & Pekka Parviainen. (2007). Multicast video performance evaluation for emergency response communications. 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. 595–604). Delft: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Group-oriented services including data dissemination, group calls and real-time video transmission are considered as an important application in public safety communications. The main interest is in one-way real-time video transmission from the hot spot to multiple recipients. This is important for efficient emergency response. The changing topology of the multi-hop communication links in a public safety environment makes routing and multicasting extremely challenging task. The purpose of this paper is to study the performance of wireless mobile ad-hoc networks with one-way multicast video traffic. To consider a realistic public safety scenario, the effect of extensive unidirectional links is investigated. The system performance study of various ad-hoc network configurations is done by simulations. For wireless multicast routing, the On Demand Multicast Routing Protocol is used. The performance results are compared with the requirements provided by Statement of Requirement document of standardization project MESA.
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Hussain Aziz Saleh. (2005). Dynamic optimisation of the use of space technology for rapid disaster response and management. In B. C. B. Van de Walle (Ed.), Proceedings of ISCRAM 2005 – 2nd International Conference on Information Systems for Crisis Response and Management (pp. 139–141). Brussels: Royal Flemish Academy of Belgium.
Abstract: Modern space and information technologies provide valuable tools for the solution of many real-world problems in fields of managing effects of natural and man-made disasters, geomatic engineering, etc. Therefore, the need to develop and optimise the use of these technologies in an efficient manner is necessary for providing reliable solutions. This paper aims to develop powerful optimisation algorithms extending current highly successful ideas of artificial intelligence for developing of the disaster warning network which is a system of satellites and ground stations for providing real time early warning of the impact of the disaster and minimise its effects (e.g., earthquakes, landslides, floods, volcanoes, etc). Such intelligent algorithms can provide a degree of functionality and flexibility suitable both for constructing high-accuracy models and in monitoring their behaviour in real time.
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