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Mark Hoogendoorn, Catholijn M. Jonker, Viara Popova, Alexei Sharpanskykh, & Lai Xu. (2005). Formal modelling and comparing of disaster plans. In B. C. B. Van de Walle (Ed.), Proceedings of ISCRAM 2005 – 2nd International Conference on Information Systems for Crisis Response and Management (pp. 97–100). Brussels: Royal Flemish Academy of Belgium.
Abstract: Every municipality in The Netherlands has its own disaster plan. A disaster plan contains the blueprint of how to handle incidents in the municipality with the aim of preventing incidents to grow into disasters. Given that each municipality has its own organisations, enterprises, infrastructure, and general layout, the disaster plans also differ. On the other hand, the disaster plans have a lot in common. Some municipalities use a common starting point, others develop their own disaster plan from scratch. In this paper two independently developed disaster plan are compared using formal modelling techniques. The analysis reveals that some interesting differences do not stem from a difference in the makings of the municipality. These differences touch the fundamentals of the communication during incident management, and might well have a critical impact in dealing with pending disasters.
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Flávio E. A. Horita, & João Porto De Albuquerque. (2013). An approach to support decision-making in disaster management based on volunteer geographic information (VGI) and spatial decision support systems (SDSS). 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. 301–306). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: The damage caused by recent events in Japan in 2011 and USA in 2012 highlighted the need to adopt measures to increase the resilience of communities against extreme events and disasters. In addition to the conventional and official information that is necessary for adaptation to disasters, recently, common citizens residents in the affected areas also began contributing with voluntary qualified and updated information. In this context, this work-in-progress presents an approach that uses voluntary information – Also known by VGI (Volunteered Geographic Information) – As a data source for Spatial Decision Support Systems (SDSS) in order to assist the decision-making in disaster management. Our approach consists of a framework that integrates voluntary and conventional data, a SDSS and processes and methods for decision-making. As a result, it is expected that this approach will assist official organizations in disaster management by providing mechanisms and information.
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Michael Howden. (2009). How humanitarian logistics information systems can improve humanitarian supply chains: A view from the field. In S. J. J. Landgren (Ed.), ISCRAM 2009 – 6th International Conference on Information Systems for Crisis Response and Management: Boundary Spanning Initiatives and New Perspectives. Gothenburg: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Humanitarian logistics represents a broad range of activities taking place within humanitarian organizations, the bulk of these activities are also components of a broader humanitarian supply chain – The network involved with providing physical aid to beneficiaries. Humanitarian logistics information systems improve information flows, which integrates logistics units more efficiently with non-logistics units within the humanitarian supply chains and provides better feedback to donors, ensuring more effective operations. Humanitarian logistics activities occur across the disaster management cycle. Humanitarian logistics information systems not only improve logistics activities in each phase, but can improve the continuity of humanitarian operations by sharing information throughout the transition of different disaster management cycle phases. Through collaboration between organizations, humanitarian logistics information systems also have the potential to reduce corruption and the market distortion which can occur during humanitarian operations.
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Hristo Tanev, Vanni Zavarella, & Josef Steinberger. (2017). Monitoring disaster impact: detecting micro-events and eyewitness reports in mainstream and social media. In eds Aurélie Montarnal Matthieu Lauras Chihab Hanachi F. B. Tina Comes (Ed.), Proceedings of the 14th International Conference on Information Systems for Crisis Response And Management (pp. 592–602). Albi, France: Iscram.
Abstract: This paper approaches the problem of monitoring the impact of the disasters by mining web sources for the events, caused by these disasters. We refer to these disaster effects as “micro-events”. Micro-events typically following a large disaster include casualties, damage on infrastructures, vehicles, services and resource supply, as well as relief operations. We present natural language grammar learning algorithms which form the basis for building micro-event detection systems from data, with no or minor human intervention, and we show how they can be applied to mainstream news and social media for monitoring disaster impact. We also experimented with applying statistical classifiers to distill, from social media situational updates on disasters, eyewitness reports from directly affected people. Finally, we describe a Twitter mining robot, which integrates some of these monitoring techniques and is intended to serve as a multilingual content hub for enhancing situational awareness.
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Chao Huang, Shifei Shen, & Quanyi Huang. (2012). An approach based on environment attributes for representation of disaster cases. 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: In this paper we overview the ongoing research into the application of case-based reasoning in emergency management, based on which we propose a new approach for representation of large-scale disaster cases. The approach takes the environmental factors into account, and the case is organized according to key scenes, rather than disaster types. Each scene consists of inherent attributes, which are concerned with the disaster type, and environment attributes, which usually facilitate the adjustment of the decision-making, and sometimes play crucial role. To describe the environment attributes, the fuzzy sets are employed to take use of the non-quantitative information. The nearness of the fuzzy sets is used to retrieve the similar case. Based on this approach, the case retrieval could even extract the case with different type but similar environment, supposing the inherent attribute is analogous. © 2012 ISCRAM.
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Humaira Waqas, & Muhammad Imran. (2019). #CampFireMissing: An Analysis of Tweets About Missing and Found People From California Wildfires. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management. Valencia, Spain: Iscram.
Abstract: Several research studies have shown the importance of social media data for humanitarian aid. Among others,
the issue of missing and lost people during disasters and emergencies is crucial for disaster managers. This work
analyzes Twitter data from a recent wildfire event to determine its usefulness for the mitigation of the missing and
found people issue. Data analysis performed using various filtering techniques, and trend analysis revealed that
Twitter contains important information potentially useful for emergency managers and volunteers to tackle this
issue. Many tweets were found containing full names, partial names, location information, and other vital clues
which could be useful for finding missing people.
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Hussain A. Syed, Marén Schorch, & Volkmar Pipek. (2020). Disaster Learning Aid: A Chatbot Centric Approach for Improved Organizational Disaster Resilience. 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. 448–457). Blacksburg, VA (USA): Virginia Tech.
Abstract: The increasingly frequent occurrence of organizational crises exemplifies the need to strengthen organizational resilience. An example of business organizations is small and medium enterprises (SMEs) which contribute largely to the economic growth. But often, their limited resources (manpower, time, financial capital), organizational structure, focus on operational routines and less priority towards disaster resilience make them more vulnerable to crisis than bigger companies. The proposed solution addresses this dilemma by establishing a collaborative medium within the organization to improve disaster resilience by raising awareness and self-learning in employees without overburdening their constrained routines and resources. Our work in progress demonstrates a conceptual model of a learning aid (collaboration channel and a chatbot) that supports the pedagogical methodologies and employs them for enhancing learnability and awareness and elaborates the usability of interactive learning instilling disaster resilience in employees and hence in an organization.
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Renato Iannella, & Karen Henricksen. (2007). Managing information in the disaster coordination centre: Lessons and opportunities. 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. 581–590). Delft: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: The current scope of ICT support for disaster coordination is primarily focused at either the network or data levels. There is significant opportunity for ICT to play an even more important role for disaster coordination at the information level. This paper reviews the information structures and requirements gathered from disaster coordination centres based on exercise observations. Such coordination of information is usually based on national frameworks that document structures, roles, and responsibilities, but are seldom supported by relevant ICT infrastructure or systems. This paper uses the lessons learned from the exercise observations to identify future opportunities for information management software to support disaster centre operations. In particular, the paper introduces a prototypical Crisis Information Management System we are developing to support two challenges: incident notification and resource messaging. The system is based on open standards under development within the OASIS standards consortium, and will be evaluated as part of future exercises.
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Soumia Ichoua. (2010). Humanitarian logistics network design for an effective disaster response. In C. Zobel B. T. S. French (Ed.), ISCRAM 2010 – 7th International Conference on Information Systems for Crisis Response and Management: Defining Crisis Management 3.0, Proceedings. Seattle, WA: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: In this paper we address the problem of pre-positioning emergency supplies prior to a disaster onset. The goal is to ensure a fast and effective response when the disaster strikes. Pre-positioning of emergency supplies is a strategic decision aimed at determining the number and location of local distribution centers as well as their inventory levels for emergency supplies. These decisions must be made in a highly disruption-prone environment where a timely response is vital and resources are scarce. We present and discuss a scenario-based model that integrates location, inventory and routing decisions.
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Ikki Niwa, Toshihiro Osaragi, Takuya Oki, & Noriaki Hirokawa. (2015). Development of Real Time Synchronous Web Application for Posting and Utilizing Disaster Information. In L. Palen, M. Buscher, T. Comes, & A. Hughes (Eds.), ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management. Kristiansand, Norway: University of Agder (UiA).
Abstract: In a large earthquake, rescue operations and fire-fighting are obstructed by fire-spreading and street-blockages. Therefore, it is important to quickly collect and utilize disaster information for disaster mitigation. In this paper, firstly, we develop a Web application for posting and viewing information collected by users in real time. Using this system, it is possible not only to easily share disaster information among users but also to apply to damage forecast such as fire-spreading. Next, we demonstrate the usefulness of the Web application by the following evaluation view points: (1) relationship between the access time of emergency vehicles from fire stations to the locations of fires and the ratio of collected information on street-blockage which is assumed to be collected with this system; (2) reciprocating time between a server and a client which is dependent on the number of users and band limitation after the occurrence of a disaster.
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Daniel Iland, Don Voita, & Elizabeth Belding. (2013). Delay tolerant disaster communication with the One Laptop per Child XO laptop. 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. 863–867). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: In this paper, we describe the design, implementation, and evaluation of a mesh network based messaging application for the One Laptop Per Child XO laptop. We outline the creation of an easy-to-use OLPC Activity that exchanges Ushahidi-style messages with nearby OLPC users through the Internet or a mesh network. Our contributions are to implement an epidemic messaging scheme on mesh networks of OLPC XO laptops, to extend the Ushahidi web application to efficiently exchange messages with nodes in mesh networks, and to allow the Ushahidi server to distribute cures, notifications of message delivery, for each received message. Testing and analysis revealed substantial overhead is introduced by the OLPC's use of Telepathy Salut for activity sharing.
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Imen Bizid, Patrice Boursier, Jacques Morcos, & Sami Faiz. (2015). A Classification Model for the Identification of Prominent Microblogs Users during a Disaster. In L. Palen, M. Buscher, T. Comes, & A. Hughes (Eds.), ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management. Kristiansand, Norway: University of Agder (UiA).
Abstract: Content shared in microblogs during disasters is expressed in various formats and languages. This diversity makes the information retrieval process more complex and computationally infeasible in real time. To address this, we propose a classification model for the identification of prominent users who are sharing relevant and exclusive information during the disaster. Users who have shared at least one tweet about the disaster are modeled using three kinds of time-sensitive features, including topical, social and geographical features. Then, these users are classified into two classes using a linear Support Vector Machine (SVM) to evaluate them over the extracted features and identify the most prominent ones. The first results using the actual dataset, show that our model has a high accuracy by detecting most of the prominent users. Moreover, we demonstrate that all the proposed features used by our model are indispensable to achieve this high accuracy.
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Muhammad Imran, Carlos Castillo, Jesse Lucas, Patrick Meier, & Jakob Rogstadius. (2014). Coordinating human and machine intelligence to classify microblog communications in crises. In and P.C. Shih. L. Plotnick M. S. P. S.R. Hiltz (Ed.), ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management (pp. 712–721). University Park, PA: The Pennsylvania State University.
Abstract: An emerging paradigm for the processing of data streams involves human and machine computation working together, allowing human intelligence to process large-scale data. We apply this approach to the classification of crisis-related messages in microblog streams. We begin by describing the platform AIDR (Artificial Intelligence for Disaster Response), which collects human annotations over time to create and maintain automatic supervised classifiers for social media messages. Next, we study two significant challenges in its design: (1) identifying which elements must be labeled by humans, and (2) determining when to ask for such annotations to be done. The first challenge is selecting the items to be labeled by crowd sourcing workers to maximize the productivity of their work. The second challenge is to schedule the work in order to reliably maintain high classification accuracy over time. We provide and validate answers to these challenges by extensive experimentation on real world datasets.
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Muhammad Imran, Shady Elbassuoni, Carlos Castillo, Fernando Díaz, & Patrick Meier. (2013). Extracting information nuggets from disaster- Related messages in social media. 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. 791–801). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: Microblogging sites such as Twitter can play a vital role in spreading information during “natural” or man-made disasters. But the volume and velocity of tweets posted during crises today tend to be extremely high, making it hard for disaster-affected communities and professional emergency responders to process the information in a timely manner. Furthermore, posts tend to vary highly in terms of their subjects and usefulness; from messages that are entirely off-topic or personal in nature, to messages containing critical information that augments situational awareness. Finding actionable information can accelerate disaster response and alleviate both property and human losses. In this paper, we describe automatic methods for extracting information from microblog posts. Specifically, we focus on extracting valuable “information nuggets”, brief, self-contained information items relevant to disaster response. Our methods leverage machine learning methods for classifying posts and information extraction. Our results, validated over one large disaster-related dataset, reveal that a careful design can yield an effective system, paving the way for more sophisticated data analysis and visualization systems.
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Gabriel Jakobson, Nandan Parameswaran, John Buford, Lundy Lewis, & Pradeep Ray. (2006). Situation-Aware multi-Agent system for disaster relief operations management. In M. T. B. Van de Walle (Ed.), Proceedings of ISCRAM 2006 – 3rd International Conference on Information Systems for Crisis Response and Management (pp. 313–324). Newark, NJ: Royal Flemish Academy of Belgium.
Abstract: Natural and human-made disasters create unparalleled challenges to Disaster Situation Management (DSM). One of the major weaknesses of the current DSM solutions is the lack of comprehensive understanding of the overall disaster operational situation, and very often making decisions based on a single event. Such weakness is clearly exhibited by the solutions based on the widely used Belief-Desire-Intention (BDI) models for building the Muiti-Agent Systems (MAS). In this work we present the adaptation of the AESOP situation management architecture to address the requirements of disaster relief operations. In particular, we extend the existing BDI model with the capability of situation awareness. We describe how the key functions of event collection, situation identification, and situation assessment are implemented in MAS architecture suitable to the characteristics of large-scale disaster recovery. We present the details of a BDI agent in this architecture including a skeleton ontology, and the distributed service architecture of the AESOP platform.
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Theresa I. Jefferson, & John R. Harrald. (2014). Estimating the impacts associated with the detonation of an improvised nuclear device. In and P.C. Shih. L. Plotnick M. S. P. S.R. Hiltz (Ed.), ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management (pp. 80–84). University Park, PA: The Pennsylvania State University.
Abstract: The explosion of an improvised nuclear device (IND), in any American city, would cause devastating physical and social impacts. These impacts would exceed the response capabilities of any city, state or region. The potential loss and suffering caused by an IND detonation can be dramatically reduced through informed planning and preparedness. By incorporating estimates of the impacts associated with the detonation of an IND into the planning process, jurisdictions can estimate the scale and scope of their response requirements. A prototype, computer-based tool was developed to quantify the human impacts associated with an IND detonation. Using various types of information such as the approximation of the prompt radiation footprint, blast footprint, and thermal footprint of the detonation, along with an estimation of the level of protection provided by building structures the system calculates the number and type of injuries that can be expected in a monocentric urban area.
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Murray E. Jennex. (2005). Informal early warning systems, the utility Y2K experience. In B. C. B. Van de Walle (Ed.), Proceedings of ISCRAM 2005 – 2nd International Conference on Information Systems for Crisis Response and Management (pp. 287–289). Brussels: Royal Flemish Academy of Belgium.
Abstract: The 2004 tsunami has generated a call for a global early warning system. Political issues may prevent this from occurring soon or at all. This paper explores previous experience with informal early warning systems from the Year 2000, Y2K, rollover. Informal early warning systems, IEWS, are cooperative systems formed outside of direct government control, usually from nonprofit or industry organizations. The two discussed utility Y2K IEWS were formed through an industry group and within a single multinational corporation. The paper concludes with lessons learned from the design and implementation of these systems.
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Murray E. Jennex. (2007). Reflections on strong angel III: Some lessons learned. 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. 537–544). Delft: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Strong Angel III was a civilian military disaster response demonstration held in San Diego in /August, 2006. This demonstration resulted in the generation of a great deal of knowledge that can potentially benefit disaster response efforts world wide. This paper attempts to capture this knowledge and to reflect on the demonstration for its value to the community.
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Jens Kersten, Anna Kruspe, Matti Wiegmann, & Friederike Klan. (2019). Robust filtering of crisis-related tweets. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management. Valencia, Spain: Iscram.
Abstract: Social media enables fast information exchange and status reporting during crises. Filtering is usually required to
identify the small fraction of social media stream data related to events. Since deep learning has recently shown to
be a reliable approach for filtering and analyzing Twitter messages, a Convolutional Neural Network is examined for
filtering crisis-related tweets in this work. The goal is to understand how to obtain accurate and robust filtering
models and how model accuracies tend to behave in case of new events. In contrast to other works, the application
to real data streams is also investigated. Motivated by the observation that machine learning model accuracies
highly depend on the used data, a new comprehensive and balanced compilation of existing data sets is proposed.
Experimental results with this data set provide valuable insights. Preliminary results from filtering a data stream
recorded during hurricane Florence in September 2018 confirm our results.
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Jan Steen Jensen, & Jan Pehrsson. (2009). Central response to large chemical accidents. In S. J. J. Landgren (Ed.), ISCRAM 2009 – 6th International Conference on Information Systems for Crisis Response and Management: Boundary Spanning Initiatives and New Perspectives. Gothenburg: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Present scenarios, issues, requirements, experience and solutions from a central government organisation supporting local emergency management organisations. The presentation will cover the experience collected in the DIADEM project by the Danish Emergency Management Organisation with the focus of using ARGOS for response to chemical incidents.
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Jeremy Diaz, Lise St. Denis, Maxwell B. Joseph, Kylen Solvik, & Jennifer K. Balch. (2020). Classifying Twitter Users for Disaster Response: A Highly Multimodal or Simple Approach? 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. 774–789). Blacksburg, VA (USA): Virginia Tech.
Abstract: We report on the development of a classifier to identify Twitter users contributing first-hand information during a disaster. Identifying such users helps social media monitoring teams identify critical information that might otherwise slip through the cracks. A parallel study (St. Denis et al., 2020) demonstrates that Twitter user filtering creates an information-rich stream of content, but the best way to approach this task is unexplored. A user's profile contains many different “modalities” of data, including numbers, text, and images. To integrate these different data types, we constructed a multimodal neural network that combines the loss function of all modalities, and we compared the results to many individual unimodal models and a decision-level fusion approach. Analysis of the results suggests that unimodal models acting on Twitter users' recent tweets are sufficient for accurate classification. We demonstrate promising classification of Twitter users for crisis response with methods that are (1) easy to implement and (2) quick to both optimize and infer.
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Johannes Anhorn, Benjamin Herfort, & João Porto de Albuquerque. (2016). Crowdsourced Validation and Updating of Dynamic Features in OpenStreetMap – An analysis of Shelter Mapping after the 2015 Nepal Earthquake. In A. Tapia, P. Antunes, V.A. Bañuls, K. Moore, & J. Porto (Eds.), ISCRAM 2016 Conference Proceedings ? 13th International Conference on Information Systems for Crisis Response and Management. Rio de Janeiro, Brasil: Federal University of Rio de Janeiro.
Abstract: The paper presents results from a validation process of OpenStreetMap (OSM) rapid mapping activities using crowdsourcing technology in the aftermath of the Gorkha earthquake 2015 in Nepal. We present a framework and tool to iteratively validate and update OSM objects. Two main objectives are addressed: first, analyzing the accuracy of the volunteered geographic information (VGI) generated by the OSM community; second, investigating the spatio-temporal dynamics of spontaneous shelter camps in Kathmandu. Results from three independent validation iterations show that only 10 % of the OSM objects are false positives (no shelter camps). Unexpectedly, previous mapping experience only had a minor influence on mapping accuracy. The results further show that it is critical to monitor the temporal dynamics. Out of 4,893 identified shelter camps, 54% were already empty/closed six days after the first mapping. So far, updating geographical features during humanitarian crisis is not properly addressed by the existing crowdsourcing approaches.
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Jonas Höchst, Lars Baumgartner, Franz Kuntke, Alvar Penning, Artur Sterz, & Bernd Freisleben. (2020). LoRa-based Device-to-Device Smartphone Communication for Crisis Scenarios. 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. 996–1011). Blacksburg, VA (USA): Virginia Tech.
Abstract: In this paper, we present an approach to facilitate long-range device-to-device communication via smartphones in crisis scenarios. Through a custom firmware for low-cost LoRa capable micro-controller boards, called rf95modem, common devices for end users can be enabled to use LoRa through a Bluetooth, Wi-Fi, or serial connection. We present two applications utilizing the flexibility provided by the proposed firmware. First, we introduce a novel device-to-device LoRa chat application that works a) on the two major mobile platforms Android and iOS and b) on traditional computers like notebooks using a console-based interface. Second, we demonstrate how other infrastructure-less technology can benefit from our approach by integrating it into the DTN7 delay-tolerant networking software. The firmware, the device-to-device chat application, the integration into DTN7, as well as the experimental evaluation code fragments are available under permissive open-source licenses.
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Jorge Vargas, Jonatan Rojas, Alejandra Inga, Wilder Mantilla, Hulber Añasco, Melanie Fatsia Basurto, et al. (2016). Towards Reliable Recurrent Disaster Forecasting Methods: Peruvian Earthquake Case. In A. Tapia, P. Antunes, V.A. Bañuls, K. Moore, & J. Porto (Eds.), ISCRAM 2016 Conference Proceedings ? 13th International Conference on Information Systems for Crisis Response and Management. Rio de Janeiro, Brasil: Federal University of Rio de Janeiro.
Abstract: We are interested in recurrent disaster forecasts; these are events such as annual cyclones in the Caribbean, earthquakes along the Ring of Fire and so on. These crises, even small- or medium-sized, are, in fact, critical for the emergency response of humanitarian organizations inasmuch as the sum of casualties and losses attained are as deadly as those that are considered exceptional. The aim of our research is to show that it is possible to use traditional forecasting methods such as: causal methods (which include the use of linear regression functions, non-linear, multivariate, etc.), time series (which include simple moving average, weighted moving average, exponential smoothing, trend-adjusted exponential smoothing, etc.) and so on, if the historical data keeps, among other criteria, its patterns, frequency, and magnitude, in a sustainable manner. Finally, an example to forecast recurrent earthquakes in Peru is presented.
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Jorge Vargas-Florez, Grovher Palomino, Andres Flores, Gloria Valdivia, Carlos Saito, Daniel Arteaga, et al. (2019). Identifying potential landslide location using Unmanned Aerial Vehicles (UAVs). In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management. Valencia, Spain: Iscram.
Abstract: The impact of landslides is determined by the previous state of vulnerability and susceptibility present in a
community. Vulnerability is related to physical aspects and susceptibility is defined as the propensity or
tendency of an area to be affected by the occurrence of a given hazard. Knowledge of geography allows us to
characterize and measure some of these factors. For example, in landslides called huaicos in Peru, these are
related to the existence of a slope and soil type of the hills favorable to the loosening of land masses, as well as
the increase in rainfall and the presence of streams. The use of UAVs (Unmanned Aerial Vehicles, commonly
called drones) for the identification of susceptibility zones is presented in this paper. The result is positive for
using the georeferenced data to identify potential landslide flow using as unique criterion surface slopes.
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