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Author Amanda Hughes; Fiona McNeill; Christopher W. Zobel pdf  isbn
openurl 
  Title 17th ISCRAM Conference Proceedings Type Conference Volume
  Year 2020 Publication (up) ISCRAM 2020 Conference Proceedings � 17th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2020  
  Volume Issue Pages 1-1193  
  Keywords  
  Abstract The 17th annual conference on Information Systems for Crisis Response and Management (ISCRAM 2020) was scheduled to be held in Blacksburg, Virginia from May 24th-27th, 2020. Unfortunately, due to the widespread impacts of the COVID-19 pandemic, the conference organizers and the ISCRAM Board decided to postpone the conference until May 2021. Even though we could not hold the conference as originally planned, all papers accepted for presentation at ISCRAM 2020 are published in the conference proceedings presented here, and the authors of these papers will have the opportunity to present their papers at the 2021 conference. The 2021 conference will once again be hosted at Virginia Tech in Blacksburg, Virginia, and it will take place during the week of May 23rd, 2021.

The theme of ISCRAM 2020 is �Bringing Disaster Resilience into Focus.� These proceedings seek to highlight resilience in Crisis and Emergency Management and to stimulate discussions that enable the design of crisis and emergency management systems that contribute to more resilient organizations and communities. We are pleased to present the accepted papers for ISCRAM 2020, which consist of excellent contributions on a wide range of topics.
 
  Address  
  Corporate Author Thesis  
  Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 978-1-949373-27-92 ISBN 2411-3478 Medium  
  Track Proceedings Expedition Conference 17th International Conference on Information Systems for Crisis Response and Management  
  Notes Approved yes  
  Call Number Serial 2307  
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Author Mehdi Ben Lazreg; Usman Anjum; Vladimir Zadorozhny; Morten Goodwin pdf  isbn
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  Title Semantic Decay Filter for Event Detection Type Conference Article
  Year 2020 Publication (up) ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2020  
  Volume Issue Pages 14-26  
  Keywords String Metric, Event Detection, Crisis Management.  
  Abstract Peaks in a time series of social media posts can be used to identify events. Using peaks in the number of posts and keyword bursts has become the go-to method for event detection from social media. However, those methods suffer from the random peaks in posts attributed to the regular daily use of social media. This paper proposes a novel approach to remedy that problem by introducing a semantic decay filter (SDF). The filter's role is to eliminate the random peaks and preserve the peak related to an event. The filter combines two relevant features, namely the number of posts and the decay in the number of similar tweets in an event-related peak. We tested the filter on three different data sets corresponding to three events: the STEM school shooting, London bridge attacks, and Virginia beach attacks. We show that, for all the events, the filter can eliminate random peaks and preserve the event-related peaks.  
  Address Dept. of Information and Communication Technology, University of Agder,Grimstad, Norway; Dept. of Informatics and Networked Systems, University of Pittsburgh, Pittsburgh, USA; Dept. of Informatics and Networked Systems, University of Pittsburgh, Pittsburgh, USA; Dept. of Information and Communication Technology, University of Agder,Grimstad, Norway  
  Corporate Author Thesis  
  Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Amanda Hughes; Fiona McNeill; Christopher W. Zobel  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 978-1-949373-27-2 ISBN 2411-3388 Medium  
  Track AI Systems for Crisis and Risks Expedition Conference 17th International Conference on Information Systems for Crisis Response and Management  
  Notes mehdi.ben.lazreg@uia.no Approved no  
  Call Number Serial 2203  
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Author Cheng Wang; Benjamin Bowes; Arash Tavakoli; Stephen Adams; Jonathan Goodall; Peter Beling pdf  isbn
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  Title Smart Stormwater Control Systems: A Reinforcement Learning Approach Type Conference Article
  Year 2020 Publication (up) ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2020  
  Volume Issue Pages 2-13  
  Keywords Reinforcement Learning, Stormwater, Flooding Control.  
  Abstract Flooding poses a significant and growing risk for many urban areas. Stormwater systems are typically used to control flooding, but are traditionally passive (i.e. have no controllable components). However, if stormwater systems are retrofitted with valves and pumps, policies for controlling them in real-time could be implemented to enhance system performance over a wider range of conditions than originally designed for. In this paper, we propose an autonomous, reinforcement learning (RL) based, stormwater control system that aims to minimize flooding during storms. With this approach, an optimal control policy can be learned by letting an RL agent interact with the system in response to received reward signals. In comparison with a set of static control rules, RL shows superior performance on a wide range of artificial storm events. This demonstrates RL's ability to learn control actions based on observation and interaction, a key benefit for dynamic and ever-changing urban areas.  
  Address Department of Engineering Systems and Environment, University of Virginia; Department of Engineering Systems and Environment, University of Virginia; Department of Engineering Systems and Environment, University of Virginia; Department of Engineering Systems and Environment, University of Virginia; Department of Engineering Systems and Environment, University of Virginia; Department of Engineering Systems and Environment, University of Virginia  
  Corporate Author Thesis  
  Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Amanda Hughes; Fiona McNeill; Christopher W. Zobel  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 978-1-949373-27-1 ISBN 2411-3387 Medium  
  Track AI Systems for Crisis and Risks Expedition Conference 17th International Conference on Information Systems for Crisis Response and Management  
  Notes cw8xk@virginia.edu Approved no  
  Call Number Serial 2202  
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Author Paulina Potemski; Nada Matta; Patrick Laclémence pdf  isbn
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  Title Modelling Women's Living Conditions' in Violence using KM techniques Type Conference Article
  Year 2020 Publication (up) ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2020  
  Volume Issue Pages 27-34  
  Keywords Women Conditions Life, Violence, Integrism Women Recruitment, Knowledge Management, Living Condition's Improvement, Tradition.  
  Abstract According to the United Nations Secretary General, gender equality has advanced in recent decades we are leaving in unprecedented global efforts to advance on women' empowerment. For example, girls' access to education has improved, the rate of child marriage declined and progress was made in the area of sexual and reproductive health and reproductive rights, including fewer maternal deaths. Nevertheless, gender equality remains a persistent challenge for countries worldwide and the lack of such equality is a major obstacle to sustainable development (Golombok et al, 1994, UNSG report, 2017). There are various inequity factors women confront. Women are the population that suffers most from different forms of discrimination. All of them root women's inferiority, women's dependence and as a matter of consequence, create a vicious circle of a domination system. Domination systems of men over women are all the more pernicious and harsher when combined with extreme poverty, remote living areas and conflicts. We discuss in this paper the fact that women are the population which underlive most difficult living conditions especially when violence and tradition are combined. Modelling life conditions put on the main factors of this violence and its consequences.  
  Address ICD, University of Technologie of Troyes, Troyes, France; ICD, University of Technologie of Troyes, Troyes, France; ICD, University of Technologie of Troyes, Troyes, France  
  Corporate Author Thesis  
  Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Amanda Hughes; Fiona McNeill; Christopher W. Zobel  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 978-1-949373-27-3 ISBN 2411-3389 Medium  
  Track AI Systems for Crisis and Risks Expedition Conference 17th International Conference on Information Systems for Crisis Response and Management  
  Notes paulina.potemski@utt.fr Approved no  
  Call Number Serial 2204  
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Author Ben Ortiz; Laura Kahn; Marc Bosch; Philip Bogden; Viveca Pavon-Harr; Onur Savas; Ian McCulloh pdf  isbn
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  Title Improving Community Resiliency and Emergency Response With Artificial Intelligence Type Conference Article
  Year 2020 Publication (up) ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2020  
  Volume Issue Pages 35-41  
  Keywords Emergency Management, Semantic Segmentation, Inland Flood Modeling, Route Optimization.  
  Abstract New crisis response and management approaches that incorporate the latest information technologies are essential in all phases of emergency preparedness and response, including the planning, response, recovery, and assessment phases. Accurate and timely information is as crucial as is rapid and coherent coordination among the responding organizations. We are working towards a multi-pronged emergency response tool that provide stakeholders timely access to comprehensive, relevant, and reliable information. The faster emergency personnel are able to analyze, disseminate and act on key information, the more effective and timelier their response will be and the greater the benefit to affected populations. Our tool consists of encoding multiple layers of open source geospatial data including flood risk location, road network strength, inundation maps that proxy inland flooding and computer vision semantic segmentation for estimating flooded areas and damaged infrastructure. These data layers are combined and used as input data for machine learning algorithms such as finding the best evacuation routes before, during and after an emergency or providing a list of available lodging for first responders in an impacted area for first. Even though our system could be used in a number of use cases where people are forced from one location to another, we demonstrate the feasibility of our system for the use case of Hurricane Florence in Lumberton, a town of 21,000 inhabitants that is 79 miles northwest of Wilmington, North Carolina.  
  Address Accenture Federal Services; Accenture Federal Services; Accenture Federal Services; Accenture Federal Services; Accenture Federal Services; Accenture Federal Services  
  Corporate Author Thesis  
  Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Amanda Hughes; Fiona McNeill; Christopher W. Zobel  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 978-1-949373-27-4 ISBN 2411-3390 Medium  
  Track AI Systems for Crisis and Risks Expedition Conference 17th International Conference on Information Systems for Crisis Response and Management  
  Notes Laura.kahn@accenturefederal.com Approved no  
  Call Number Serial 2205  
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Author Rahul Pandey; Brenda Bannan; Hemant Purohit pdf  isbn
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  Title CitizenHelper-training: AI-infused System for Multimodal Analytics to assist Training Exercise Debriefs at Emergency Services Type Conference Article
  Year 2020 Publication (up) ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2020  
  Volume Issue Pages 42-53  
  Keywords Training Exercise, Emergency Preparedness, AI system, Learning Analytics, Responder Training.  
  Abstract The adoption of Artificial Intelligence (AI) technologies across various real-world applications for human performance augmentation demonstrates an unprecedented opportunity for emergency management. However, the current exploration of AI technologies such as computer vision and natural language processing is highly focused on emergency response and less investigated for the preparedness and mitigation phases. The training exercises for emergency services are critical to preparing responders to perform effectively in the real-world, providing a venue to leverage AI technologies. In this paper, we demonstrate an application of AI to address the challenges in augmenting the performance of instructors or trainers in such training exercises in real-time, with the explicit aim of reducing cognitive overload in extracting relevant knowledge from the voluminous multimodal data including video recordings and IoT sensor streams. We present an AI-infused system design for multimodal stream analytics and lessons from its use during a regional training exercise for active violence events.  
  Address George Mason University; George Mason University; George Mason University  
  Corporate Author Thesis  
  Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Amanda Hughes; Fiona McNeill; Christopher W. Zobel  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 978-1-949373-27-5 ISBN 2411-3391 Medium  
  Track AI Systems for Crisis and Risks Expedition Conference 17th International Conference on Information Systems for Crisis Response and Management  
  Notes rpandey4@gmu.edu Approved no  
  Call Number Serial 2206  
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Author Samer Chehade; Nada Matta; Jean-Baptiste Pothin; Remi Cogranne pdf  isbn
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  Title Ontology-Based Approach for Designing User Interfaces: Application for Rescue Actors Type Conference Article
  Year 2020 Publication (up) ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2020  
  Volume Issue Pages 54-65  
  Keywords Interactions, Modelling, Ontologies, Rescue Operations, User Interface Design.  
  Abstract Nowadays, rescue actors still lack backing to exchange information effectively and ensure a common operational picture. Several studies report a low adoption of communication systems in rescue operations as well as a negative position of actors to such systems. The real needs of stakeholders, simply put, are not satisfied by the offered systems. Observing this circumstance through a user-centred design focal point, we notice that such issues ordinarily originate from inadequate design techniques. For this reason, we aim to implement Rescue MODES, a communication system oriented to support awareness amongst French actors in rescue operations based on their needs. In this paper, we propose an approach and introduce a platform that allows final users to design system interfaces in a customised way. This approach is based on an application ontology and an interaction model.  
  Address Department of Research and Development, DataHertz, Troyes, France; Institut Charles Delaunay, TechCICO, Université de Technologie de Troyes, Troyes, France; Department of Research and Development, DataHertz, Troyes, France; Institut Charles Delaunay, M2S, Université de Technologie de Troyes, Troyes, France  
  Corporate Author Thesis  
  Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Amanda Hughes; Fiona McNeill; Christopher W. Zobel  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 978-1-949373-27-6 ISBN 2411-3392 Medium  
  Track AI Systems for Crisis and Risks Expedition Conference 17th International Conference on Information Systems for Crisis Response and Management  
  Notes Samer.chehade@datahertz.fr Approved no  
  Call Number Serial 2207  
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Author Mirko Zaffaroni; Claudio Rossi pdf  isbn
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  Title Water Segmentation with Deep Learning Models for Flood Detection and Monitoring Type Conference Article
  Year 2020 Publication (up) ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2020  
  Volume Issue Pages 66-74  
  Keywords Deep Learning, Water Segmentation, Data Validation.  
  Abstract Flooding is a natural hazard that causes a lot of deaths every year and the number of flood events is increasing worldwide because of climate change effects. Detecting and monitoring floods is of paramount importance in order to reduce their impacts both in terms of affected people and economic losses. Automated image analysis techniques capable to extract the amount of water from a picture can be used to create novel services aimed to detect floods from fixed surveillance cameras, drones, crowdsourced in-field observations, as well as to extract meaningful data from social media streams. In this work we compare the accuracy and the prediction performances of recent Deep Learning algorithms for the pixel-wise water segmentation task. Moreover, we release a new dataset that enhances well-know benchmark datasets used for multi-class segmentation with specific flood-related images taken from drones, in-field observations and social media.  
  Address LINKS Foundation, University of Turin, Computer Science Department; LINKS Foundation  
  Corporate Author Thesis  
  Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Amanda Hughes; Fiona McNeill; Christopher W. Zobel  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 978-1-949373-27-7 ISBN 2411-3393 Medium  
  Track AI Systems for Crisis and Risks Expedition Conference 17th International Conference on Information Systems for Crisis Response and Management  
  Notes mirko.zaffaroni@linksfoundation.com Approved no  
  Call Number Serial 2208  
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Author Gerasimos Antzoulatos; Panagiotis Giannakeris; Ilias Koulalis; Anastasios Karakostas; Stefanos Vrochidis; Ioannis Kompatsiaris pdf  isbn
openurl 
  Title A Multi-Layer Fusion Approach For Real-Time Fire Severity Assessment Based on Multimedia Incidents Type Conference Article
  Year 2020 Publication (up) ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2020  
  Volume Issue Pages 75-89  
  Keywords Crisis Management, Real-Time Fire Severity Assessment, Image Recognition, Object Detection, Semantic Segmentation.  
  Abstract Shock forest fires have short and long-terms devastating impact on the sustainable management and viability of natural, cultural and residential environments, the local and regional economies and societies. Thus, the utilisation of risk-based decision support systems which encapsulate the technological achievements in Geographical Information Systems (GIS) and fire growth simulation models have rapidly increased in the last decades. On the other hand, the rise of image and video capturing technology, the usage mobile and wearable devices, and the availability of large amounts of multimedia in social media or other online repositories has increased the interest in the image understanding domain. Recent computer vision techniques endeavour to solve several societal problems with security and safety domains to be one of the most serious amongst others. Out of the millions of images that exist online in social media or news articles a great deal of them might include the existence of a crisis or emergency event. In this work, we propose a Multi-Layer Fusion framework, for Real-Time Fire Severity Assessment, based on knowledge extracted from the analysis of Fire Multimedia Incidents. Our approach consists of two levels: (a) an Early Fusion level, in which state-of-the-art image understanding techniques are deployed so as to discover fire incidents and objects from images, and (b) the Decision Fusion level which combines multiple fire incident reports aiming to assess the severity of the ongoing fire event. We evaluate our image understanding techniques in a collection of public fire image databases, and generate simulated incidents and feed them to our Decision Fusion level so as to showcase our method's applicability.  
  Address Information Technologies Institute (ITI) – Centre for Research and Technology Hellas (CERTH); Information Technologies Institute (ITI) – Centre for Research and Technology Hellas (CERTH); Information Technologies Institute (ITI) – Centre for Research and Technology Hellas (CERTH);Information Technologies Institute (ITI) – Centre for Research and Technology Hellas (CERTH);Information Technologies Institute (ITI) – Centre for Research and Technology Hellas (CERTH);Information Technologies Institute (ITI) – Centre for Research and Technology Hellas (CERTH)  
  Corporate Author Thesis  
  Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Amanda Hughes; Fiona McNeill; Christopher W. Zobel  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 978-1-949373-27-8 ISBN 2411-3394 Medium  
  Track AI Systems for Crisis and Risks Expedition Conference 17th International Conference on Information Systems for Crisis Response and Management  
  Notes gantzoulatos@iti.gr Approved no  
  Call Number Serial 2209  
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Author Dipak Singh; Shayan Shams; Joohyun Kim; Seung-jong Park; Seungwon Yang pdf  isbn
openurl 
  Title Fighting for Information Credibility: AnEnd-to-End Framework to Identify FakeNews during Natural Disasters Type Conference Article
  Year 2020 Publication (up) ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2020  
  Volume Issue Pages 90-99  
  Keywords Neural Networks, Social Network, Natural Disaster, Fake News, Deep Learning.  
  Abstract Fast-spreading fake news has become an epidemic in the post-truth world of politics, the stock market, or even during natural disasters. A large amount of unverified information may reach a vast audience quickly via social media. The effect of misinformation (false) and disinformation (deliberately false) is more severe during the critical time of natural disasters such as flooding, hurricanes, or earthquakes. This can lead to disruptions in rescue missions and recovery activities, costing human lives and delaying the time needed for affected communities to return to normal. In this paper, we designed a comprehensive framework which is capable of developing a training set and trains a deep learning model for detecting fake news events occurring during disasters. Our proposed framework includes infrastructure to collect Twitter posts which spread false information. In our model implementation, we utilized the Transfer Learning scheme to transfer knowledge gained from a large and general fake news dataset to relatively smaller fake news events occurring during disasters as a means of overcoming the limited size of our training dataset. Our detection model was able to achieve an accuracy of 91.47\% and F1 score of 90.89 when it was trained with the first 28 hours of Twitter data. Our vision for this study is to help emergency managers during disaster response with our framework so that they may perform their rescue and recovery actions effectively and efficiently without being distracted by false information.  
  Address Louisiana State University; University of Texas; Louisiana State University; Louisiana State University;Louisiana State University  
  Corporate Author Thesis  
  Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Amanda Hughes; Fiona McNeill; Christopher W. Zobel  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 978-1-949373-27-9 ISBN 2411-3395 Medium  
  Track AI Systems for Crisis and Risks Expedition Conference 17th International Conference on Information Systems for Crisis Response and Management  
  Notes dsingh8@lsu.edu Approved no  
  Call Number Serial 2210  
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Author Nilani Algiriyage; Raj Prasanna; Emma E H Doyle; Kristin Stock; David Johnston pdf  isbn
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  Title Traffic Flow Estimation based on Deep Learning for Emergency Traffic Management using CCTV Images Type Conference Article
  Year 2020 Publication (up) ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2020  
  Volume Issue Pages 100-109  
  Keywords CCTV Big Data, YOLOv3, Traffic Flow Estimation.  
  Abstract Emergency Traffic Management (ETM) is one of the main problems in smart urban cities. This paper focuses on selecting an appropriate object detection model for identifying and counting vehicles from closed-circuit television (CCTV) images and then estimating traffic flow as the first step in a broader project. Therefore, a case is selected at one of the busiest roads in Christchurch, New Zealand. Two experiments were conducted in this research; 1) to evaluate the accuracy and speed of three famous object detection models namely faster R-CNN, mask R-CNN and YOLOv3 for the data set, 2) to estimate the traffic flow by counting the number of vehicles in each of the four classes such as car, bus, truck and motorcycle. A simple Region of Interest (ROI) heuristic algorithm is used to classify vehicle movement direction such as \quotes{left-lane} and \quotes{right-lane}. This paper presents the early results and discusses the next steps.  
  Address Joint Centre for Disaster Research, Massey University; Joint Centre for Disaster Research, Massey University; Joint Centre for Disaster Research, Massey University; Institute of Natural and Mathematical Sciences, Massey University; Joint Centre for Disaster Research, Massey University;  
  Corporate Author Thesis  
  Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Amanda Hughes; Fiona McNeill; Christopher W. Zobel  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 978-1-949373-27-10 ISBN 2411-3396 Medium  
  Track AI Systems for Crisis and Risks Expedition Conference 17th International Conference on Information Systems for Crisis Response and Management  
  Notes r.nilani@massey.ac.nz Approved no  
  Call Number Serial 2211  
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Author Dashley K. Rouwendal van Schijndel; Jo E. Hannay; Audun Stolpe pdf  isbn
openurl 
  Title Simulation Vignette Generation from Answer Set Specifications Type Conference Article
  Year 2020 Publication (up) ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2020  
  Volume Issue Pages 110-121  
  Keywords Exercise Management; Answer Set Programming; Mixed Reality Simulations; Vignette Generation  
  Abstract We investigate an approach that allows exercise managers to design simulations with an explicit focus on building skills, rather than having to focus on all the objects and interactions that a simulation must have. Exercise managers may design exercises at various levels of abstraction and always independently of how those sessions are implemented in simulations, while simulation components that implement the design are assembled and to some extent, automatically, behind the scenes. We outline (1) how Answer Set Programming can assist exercise managers in exercise planning and (2) how automated stage and content generation may be used to invoke appropriate simulation components to realize the design. For deliberate and recurrent training of decision-making skills, stages and content must vary to avoid familiarity (testing effects). We conclude by distilling a main research hypothesis that stipulates how (1) and (2) represent two modes of automated reasoning (so-called deductive versus abductive) and how that distinction clarifies the planning task.  
  Address University of Oslo, Department of Technology Systems; Norwegian Computing Center, Department of Applied Research in Information Technology; University of Oslo, Department of Technology Systems, Norwegian Computing Center, Department of Applied Research in Information Technology  
  Corporate Author Thesis  
  Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Amanda Hughes; Fiona McNeill; Christopher W. Zobel  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 978-1-949373-27-11 ISBN 2411-3397 Medium  
  Track AI Systems for Crisis and Risks Expedition Conference 17th International Conference on Information Systems for Crisis Response and Management  
  Notes d.k.rouwendal@its.uio.no Approved no  
  Call Number Serial 2212  
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Author Md Fitrat Hossain; Thomas Kissane; Priyanka Annapureddy; Wylie Frydrychowicz; Sheikh Iqbal Ahamed; Naveen Bansal; Praveen Madiraju; Niharika Jain; Mark Flower; Katinka Hooyer; Lisa Rein; Zeno Franco pdf  isbn
openurl 
  Title Implementing Algorithmic Crisis Alerts in mHealth Systems for Veterans with PTSD Type Conference Article
  Year 2020 Publication (up) ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2020  
  Volume Issue Pages 122-133  
  Keywords Crisis; Machine Learning Algorithms; mHealth; PTSD  
  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.  
  Address Marquette University; Marquette University; Marquette University; Marquette University; Marquette University; Marquette University; Marquette University; Marquette University; Mental Health America; Medical College of Wisconsin; Medical College of Wisconsin; Medical College of Wisconsin  
  Corporate Author Thesis  
  Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Amanda Hughes; Fiona McNeill; Christopher W. Zobel  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 978-1-949373-27-12 ISBN 2411-3398 Medium  
  Track AI Systems for Crisis and Risks Expedition Conference 17th International Conference on Information Systems for Crisis Response and Management  
  Notes mdfitrat.hossain@marquette.edu Approved no  
  Call Number Serial 2213  
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Author Julien Coche; Aurelie Montarnal; Andrea Tapia; Frederick Benaben pdf  isbn
openurl 
  Title Automatic Information Retrieval from Tweets: A Semantic Clustering Approach Type Conference Article
  Year 2020 Publication (up) ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2020  
  Volume Issue Pages 134-141  
  Keywords Information Retrieval; Word Embedding; BERT  
  Abstract Much has been said about the value of social media messages for emergency services. The new uses related to these platforms bring users to share information, otherwise unknown in crisis events. Thus, many studies have been performed in order to identify tweets relating to a crisis event or to classify these tweets according to certain categories. However, determining the relevant information contained in the messages collected remains the responsibility of the emergency services. In this article, we introduce the issue of classifying the information contained in the messages. To do so, we use classes such as those used by the operators in the call centers. Particularly we show that this problem is related to named entities recognition on tweets. We then explain that a semi-supervised approach might be beneficial, as the volume of data to perform this task is low. In a second part, we present some of the challenges raised by this problematic and different ways to answer it. Finally, we explore one of them and its possible outcomes.  
  Address IMT Mines Albi; IMT Mines Albi; Penn State University; IMT Mines Albi  
  Corporate Author Thesis  
  Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Amanda Hughes; Fiona McNeill; Christopher W. Zobel  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 978-1-949373-27-13 ISBN 2411-3399 Medium  
  Track AI Systems for Crisis and Risks Expedition Conference 17th International Conference on Information Systems for Crisis Response and Management  
  Notes julien.coche@mines-albi.fr Approved no  
  Call Number Serial 2214  
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Author Toshihiro Osaragi pdf  isbn
openurl 
  Title Accessibility Evaluation of Specific Emergency Transportation Roads and Benefits of Seismic Retrofits on Buildings Adjoining Roads Type Conference Article
  Year 2020 Publication (up) ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2020  
  Volume Issue Pages 143-156  
  Keywords Accessibility of Emergency Vehicle, Specific Emergency Transportation Road, Quake-Resistant-Conversion of Building, Large Earthquake.  
  Abstract Securing the accessibility of emergency vehicles using specific emergency transportation road (SETR) is crucial for the rapid activities of emergency vehicles after a large earthquake. In this paper, we construct a simulation model that describes collapse of roadside buildings and following street blockages, and evaluate the accessibility of emergency vehicles. Performing the simulations, we demonstrate the effects of quake-resistant-conversion of roadside buildings as follows: (1) the accessibility of emergency vehicles using SETR is not good enough under the current situation, but (2) can be significantly improved by performing seismic retrofit of buildings according to seismic index of building structure.  
  Address Tokyo Institute of Technology  
  Corporate Author Thesis  
  Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Amanda Hughes; Fiona McNeill; Christopher W. Zobel  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 978-1-949373-27-14 ISBN 2411-3400 Medium  
  Track Analytical Modeling and Simulation Expedition Conference 17th International Conference on Information Systems for Crisis Response and Management  
  Notes osaragi.t.aa@m.titech.ac.jp Approved no  
  Call Number Serial 2215  
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Author Flavio Dusse; Renato Novais; Manoel Mendonça pdf  isbn
openurl 
  Title A Visual Analytics Based Model for Crisis Management Decision-Making Type Conference Article
  Year 2020 Publication (up) ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2020  
  Volume Issue Pages 157-166  
  Keywords Crisis Management, Decision-Making, Visual Analytics, Model.  
  Abstract Crisis Management (CM) refers to the ability to deal with crisis tasks in different phases and iterations. People working in a crisis are generally under pressure to make the right decision at the right time. They must process large amounts of data and assimilate the received information in an intuitive way. Visual Analytics (VA) is potentially useful to analyze and understand the huge amount of data in several areas including in a crisis. We propose a model based on VA to support decision-making in CM. The aim of the model is to help visualization designers to create effective VA interfaces, to help crisis managers to make quick and assertive decisions with them. In previous studies, we carried out a survey protocol with a multi-method approach to collect data on crisis related decision-making and analyze all these data qualitatively with formal techniques during the large events held in Brazil in recent years. In this work, we used our previous findings to develop the proposed model. We validated it using the focus group technique. With the new findings, we identified relevant insights on the use of VA for crisis management. We hope that, with these continuous cycles of validation and improvement, the agencies that manage crises might use our model as a reference for building more effective IT decision-making infrastructures based on VA.  
  Address Federal University of Bahia; Federal Institute of Bahia; Federal University of Bahia  
  Corporate Author Thesis  
  Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Amanda Hughes; Fiona McNeill; Christopher W. Zobel  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 978-1-949373-27-15 ISBN 2411-3401 Medium  
  Track Analytical Modeling and Simulation Expedition Conference 17th International Conference on Information Systems for Crisis Response and Management  
  Notes dussebr@dcc.ufba.br Approved no  
  Call Number Serial 2216  
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Author Erik Prytz; Anna-Maria Grönbäck; Krisjanis Steins; Craig Goolsby; Tobias Andersson Granberg; Carl-Oscar Jonson pdf  isbn
openurl 
  Title Evaluating the Effect of Bleeding Control Kit Locations for a Mass Casualty Incident Using Discrete Event Simulation Type Conference Article
  Year 2020 Publication (up) ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2020  
  Volume Issue Pages 167-178  
  Keywords Simulation, Mass Casualty Incident, Tourniquet, Stop the Bleed, Bleeding Control Kit Placement.  
  Abstract The purpose of this study was to develop a simulation model to evaluate bleeding control kit location strategies for a mass casualty incident scenario. Specifically, the event simulated was an explosion at a large sports arena. The model included a representation of the arena itself, simulated crowd movements following the detonation of an improvised explosive device, injuries and treatments, and different ways for immediate responders to help injured patients using tourniquets. The simulation model gave logically consistent results in the validation scenarios and the simulation outcomes were in line with the expected outcomes. The results of the different tourniquet location scenarios indicated that decentralized placement (more than one location) is better, easy access is important (between rather than at emergency exits) and that an increased number of available tourniquets will result in an increased number of survivors.  
  Address Department of Computer and Information Science, Linköping University; Linköping University; Department of Science and Technology, Linköping University; Uniformed Services University of the Health Sciences; Department of Science and Technology, Linköping University; Center for Disaster Medicine and Traumatology, and Department of Biomedical and Clinical Sciences, Linköping University  
  Corporate Author Thesis  
  Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Amanda Hughes; Fiona McNeill; Christopher W. Zobel  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 978-1-949373-27-16 ISBN 2411-3402 Medium  
  Track Analytical Modeling and Simulation Expedition Conference 17th International Conference on Information Systems for Crisis Response and Management  
  Notes Erik.prytz@liu.se Approved no  
  Call Number Serial 2217  
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Author Laura Szczyrba; Yang Zhang; Duygu Pamukcu; Derya Ipek Eroglu pdf  isbn
openurl 
  Title A Machine Learning Method to Quantify the Role of Vulnerability in Hurricane Damage Type Conference Article
  Year 2020 Publication (up) ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2020  
  Volume Issue Pages 179-187  
  Keywords Vulnerability, Impact, Damage, Machine Learning, Hurricane María.  
  Abstract Accurate pre-disaster damage predictions and post-disaster damage assessments are challenging because of the complicated interrelationships between multiple damage drivers, including various natural hazards, as well as antecedent infrastructure quality and demographic characteristics. Ensemble decision trees, a family of machine learning algorithms, are well suited to quantify the role of social vulnerability in disaster impacts because they provide interpretable measures of variable importance for predictions. Our research explores the utility of an ensemble decision tree algorithm, Random Forest Regression, for quantifying the role of vulnerability with a case study of Hurricane Mar\'ia. The contributing predictive power of eight drivers of structural damage was calculated as the decrease in model mean squared error. A measure of social vulnerability was found to be the model's leading predictor of damage patterns. An additional algorithm, other methods of quantifying variable importance, and future work are discussed.  
  Address Virginia Tech; Virginia Tech; Virginia Tech; Virginia Tech  
  Corporate Author Thesis  
  Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Amanda Hughes; Fiona McNeill; Christopher W. Zobel  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 978-1-949373-27-17 ISBN 2411-3403 Medium  
  Track Analytical Modeling and Simulation Expedition Conference 17th International Conference on Information Systems for Crisis Response and Management  
  Notes lszczyrba@vt.edu Approved no  
  Call Number Serial 2218  
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Author Xiaoyan Zhang; Graham Coates; Sarah Dunn; Jean Hall pdf  isbn
openurl 
  Title Emergency Evacuation from a Multi-floor Building using Agent-based Modeling Type Conference Article
  Year 2020 Publication (up) ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2020  
  Volume Issue Pages 188-199  
  Keywords Emergency Evacuation, Agent-based Modeling and Simulation, Multi-floor Building.  
  Abstract This paper presents an overview of the ongoing research into the development of an agent-based model to enable simulations to be performed of agents evacuating from a multi-floor building with a complex layout, including staircases. Specifically, a flow field of navigation objects is constructed pre-computation, which stores the directions and shortest distances to all exits and staircases. Using the flow field, a navigation method is proposed for agents familiar with the environment to identify and follow the shortest route to a chosen exit. Preliminary simulations have been performed to investigate the effect on evacuation time of (i) exit configurations and (ii) familiarity of agents with the building layout. In assessing the effect of exit configurations, results show that the location of the main entrance has a significant influence on evacuation time. In addition, having more exits does not necessarily lead to a shorter evacuation time. In terms of the effect of familiarity of agents, having more agents with a greater level of familiarity does not significantly reduce evacuation time in most cases.  
  Address Newcastle University; Newcastle University; Newcastle University; Newcastle University  
  Corporate Author Thesis  
  Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Amanda Hughes; Fiona McNeill; Christopher W. Zobel  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 978-1-949373-27-18 ISBN 2411-3404 Medium  
  Track Analytical Modeling and Simulation Expedition Conference 17th International Conference on Information Systems for Crisis Response and Management  
  Notes X.Zhang110@newcastle.ac.uk Approved no  
  Call Number Serial 2219  
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Author Anying Chen; Zhongliang Huang; Manchun Liang; Guofeng Su pdf  isbn
openurl 
  Title Empirical Study of Individual Evacuation Decision-making in Fire Accidents: Evacuate Intention and Herding Effect Type Conference Article
  Year 2020 Publication (up) ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2020  
  Volume Issue Pages 200-209  
  Keywords Fire Accidents, Evacuation Experiment, Evacuate Intention, Herding Behaviors.  
  Abstract People's decision of evacuating or not could greatly influence the final losses in fire accidents. In order to study people's response under emergent occasions, a fire accident evacuation drill experiment was conducted in an office building without advance notice. 113 Participants' response and their decision-making process were collected by questionnaire survey right after the experiment. In this study, we mainly focused on two aspects of people's response, including participants' evacuate intention and their herding tendency during evacuate decision-making. It is found that the classical Expected Utility Theory (EUT) has certain limitation in explaining individual's evacuation intention, but the relationship between the expected utility and the evacuation intention could be represented with a modified model based on EUT. Furthermore, the herding tendency is found to be different for the two groups of people who intend to evacuate and not to evacuate. People who firstly intend not to evacuate are more easily to form herding behavior and change their minds to evacuate. Based on these findings, models of individual evacuation intention and herding tendency for two groups of people are put forward. Simulation is conducted to investigate the effect of these two changes in people's evacuation decision-making process, and results show that they both increase the final evacuation rate, reflecting the majority's risk aversion characteristics.  
  Address Tsinghua University;Tsinghua University; Tsinghua University; Tsinghua University  
  Corporate Author Thesis  
  Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Amanda Hughes; Fiona McNeill; Christopher W. Zobel  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 978-1-949373-27-19 ISBN 2411-3405 Medium  
  Track Analytical Modeling and Simulation Expedition Conference 17th International Conference on Information Systems for Crisis Response and Management  
  Notes chenay15@mails.tsinghua.edu.cn Approved no  
  Call Number Serial 2220  
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Author Milad Baghersad; Christopher W. Zobel; Ravi Behara pdf  isbn
openurl 
  Title Evaluation of Local Government Performance after Disasters Type Conference Article
  Year 2020 Publication (up) ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2020  
  Volume Issue Pages 210-217  
  Keywords 311 Services, Disaster, Municipal Departments, Resilience.  
  Abstract Monitoring and evaluation can help organizations involved in disasters learn from their responses to prior events and improve their performance over time. Using a data set of non-emergency service requests in New York City (NYC), this paper provides a method to evaluate and compare the performance of local governments in terms of service request response times after different disaster events. In particular, the proposed method can be used to compare such performance across divisions or boroughs in a city. To illustrate this, we evaluate the performance in five of NYC's boroughs: the Bronx, Brooklyn, Manhattan, Queens, and Staten Island, across seven major natural disaster events from 2010 to 2012. Our analyses show that Queens and Brooklyn demonstrate better performance than the other boroughs in almost all of the seven events under consideration.  
  Address Florida Atlantic University; Virginia Tech; Florida Atlantic University  
  Corporate Author Thesis  
  Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Amanda Hughes; Fiona McNeill; Christopher W. Zobel  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 978-1-949373-27-20 ISBN 2411-3406 Medium  
  Track Analytical Modeling and Simulation Expedition Conference 17th International Conference on Information Systems for Crisis Response and Management  
  Notes mbaghersad@fau.edu Approved no  
  Call Number Serial 2221  
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Author Eva Petitdemange; Elyes Lamine; Franck Fontanili; Matthieu Lauras pdf  isbn
openurl 
  Title Enhancing Emergency Call Centers' Performance Through a Data-driven Simulation Approach Type Conference Article
  Year 2020 Publication (up) ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2020  
  Volume Issue Pages 218-227  
  Keywords Emergency Call Center, Performance, Simulation, Data-Driven, Continuous Improvement, Organization.  
  Abstract Emergency Call Centers (ECCs) can be considered as the starting point of the pre-hospital emergency medical system. Although, ECCs exist everywhere, their business processes and their performance levels differ from one place to another, even sometimes in a same country. By definition, users expect a high level of performance, particularly regarding the waiting time and the processing time of the calls. Additionally, ECCs might have difficulties to manage sudden rise of activities following disasters impacting huge number of victims for instance. To support ECCs in their continuous improvement steps, this paper suggests an innovative framework and its associated tools to support both diagnosis of current organizations and enhancement of their performance. Concretely, the proposal is data-driven and simulation oriented. First experiments are shown in order to demonstrate the potential benefits of such an approach. Avenues for further research are also discussed.  
  Address IMT Mines Albi, University of Toulouse; IMT Mines Albi, University of Toulouse; IMT Mines Albi, University of Toulouse; IMT Mines Albi, University of Toulouse  
  Corporate Author Thesis  
  Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Amanda Hughes; Fiona McNeill; Christopher W. Zobel  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 978-1-949373-27-21 ISBN 2411-3407 Medium  
  Track Analytical Modeling and Simulation Expedition Conference 17th International Conference on Information Systems for Crisis Response and Management  
  Notes eva.petitdemange@mines-albi.fr Approved no  
  Call Number Serial 2222  
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Author Duygu Pamukcu; Christopher W. Zobel; Andrew Arnette pdf  isbn
openurl 
  Title Characterizing Social Community Structures in Emergency Shelter Planning Type Conference Article
  Year 2020 Publication (up) ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2020  
  Volume Issue Pages 228-236  
  Keywords Evacuation Planning; Sheltering; Simulation; Social Network; Group Behavior  
  Abstract During emergencies, it is often necessary to evacuate vulnerable people to safer places to reduce loss of lives and cope with human suffering. Shelters are publically available places to evacuate, especially for people who do not have any other choices. This paper overviews emergency shelter planning in disaster mitigation and preparation and discusses the need for better responding to people who need to evacuate during emergencies. Recent evacuation studies pay attention to integrating social factors into evacuation modeling for better prediction of evacuation decisions. Our goal is to address the impact of social behavior on the sheltering choices of evacuees and to explore the potential contributions of including social network characteristics in the decision-making process of authorities. We present the shelter utilization problem in South Carolina during Hurricane Florence and discuss an agent-based modeling approach that considers social community structures in modeling the shelter choice behavior of socially connected individuals.  
  Address Virginia Tech; Virginia Tech; University of Wyoming  
  Corporate Author Thesis  
  Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Amanda Hughes; Fiona McNeill; Christopher W. Zobel  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 978-1-949373-27-22 ISBN 2411-3408 Medium  
  Track Analytical Modeling and Simulation Expedition Conference 17th International Conference on Information Systems for Crisis Response and Management  
  Notes duygu@vt.edu Approved no  
  Call Number Serial 2223  
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Author Andrew Arnette; Christopher W. Zobel; Duygu Pamukcu pdf  isbn
openurl 
  Title Post-Impact Analysis of Disaster Relief Resource Pre-Positioning After the 2013 Colorado Floods Type Conference Article
  Year 2020 Publication (up) ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2020  
  Volume Issue Pages 237-243  
  Keywords Disaster Operations Management; Facility Location; Humanitarian Operations  
  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.  
  Address University of Wyoming; Virginia Tech; Virginia Tech  
  Corporate Author Thesis  
  Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Amanda Hughes; Fiona McNeill; Christopher W. Zobel  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 978-1-949373-27-23 ISBN 2411-3409 Medium  
  Track Analytical Modeling and Simulation Expedition Conference 17th International Conference on Information Systems for Crisis Response and Management  
  Notes aarnette@uwyo.edu Approved no  
  Call Number Serial 2224  
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Author Cornelius Dold; Christopher Munschauer; Ompe Aimé Mudimu pdf  isbn
openurl 
  Title Real-Life Exercises as a Tool in Security Research and Civil Protection – Options for Data Collections Type Conference Article
  Year 2020 Publication (up) ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2020  
  Volume Issue Pages 244-250  
  Keywords Real-Life Exercises; Data Collection; Emergency Response; Civil Protection; Large-Scale Exercises  
  Abstract A real-life exercise is a scientific method used by the TH Köln to generate data sets of new technologies and operational concepts derived from research projects. The Institute of Rescue Engineering and Civil Protection (German acronym: IRG) uses a real-time locating system (RTLS), video surveillance, observers and a mass casualty incident benchmark to generate motion profiles, information flows and information on the quality of care. In this practitioner paper these different methods will be discussed and the combination of different data is described. Furthermore, an outlook is given on the extent to which the method will be improved and expand-ed in the future. Concluding it can be said that the combination of all collected data is essential for the evalua-tion of a real-life exercise in security research or civil protection.  
  Address TH Köln – University of Applied Sciences, Cologne; TH Köln – University of Applied Sciences, Cologne; TH Köln – University of Applied Sciences, Cologne  
  Corporate Author Thesis  
  Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Amanda Hughes; Fiona McNeill; Christopher W. Zobel  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 978-1-949373-27-24 ISBN 2411-3410 Medium  
  Track Analytical Modeling and Simulation Expedition Conference 17th International Conference on Information Systems for Crisis Response and Management  
  Notes cornelius.dold@th-koeln.de Approved no  
  Call Number Serial 2225  
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