Abstract: Telephone calls are the predominant telecommunication mode in Sri Lanka. Leveraging voice-based applications for disaster communication would be acceptable and sustainable. The findings in this paper are from an experiment concerning interactive voice for connecting community-based emergency field operatives with their central coordination hub. Challenge was in interchanging the Freedom Fone (FF) Interactive Voice Response (IVR) generated, Sinhala and Tamil language, speech data with the text-based 'Sahana' disaster management system for analysis and decision support. Emergency Data Exchange Language (EDXL) interoperable content standard was adopted for mediation. Low quality voice data resulting in incomplete information was a barrier to automating transformations between text and speech. Replacing those processes with human procedure significantly degrades the reliability. Moreover, human interaction with decoupled software systems, to accomplish the sequence of tasks, points to instabilities. This paper discusses the complexities and usability shortcomings discovered through controlled-exercises in Sri Lanka. © 2012 ISCRAM.

Abstract: Microblogging platforms have been increasingly used in crisis, facilitating more participatory communication between official response channels and affected communities. Despite the potential benefits, research has shown that disaster response organizations could not effectively utilize social media data due to data deluge (Knox 2022). To better understand the information needed for disaster response, we turn to the National Incident Management System Guidance for public information officers (PIOs) (NIMS Basic Guidance for PIOs 2020), the primary spokesperson for emergency management organizations. The guidance indicates that PIOs use social media for two major purposes, supporting their operational needs and gauging public perception of risk and incident response. To support the operational needs, the crisis informatics literature has heavily focused on information types supporting situational awareness, including serviceable, eyewitness or actionable information. However, the information representing public perception, such as people’s cognitive and perceptual processes in response to incidents, has been less addressed at scale. To bridge the gap between quantitative study in crisis informatics and information representing cognitive and perceptual processes and better support the task of PIOs, I focus on the study of people’s cognitive and perceptual processes on social media for my research. Cognitive and perceptual processes refer to the way that people pay attention to or process environmental inputs, including the mental activities of acquisition, processing or evaluation of environmental cues, social cues, and warnings. These processes reveal people’s perception of- and decision-making in response to potential threats. With this focus, I seek to answer the following research question: How could people’s cognitive and perceptual processes be inferred from their social media activities in crisis to benefit stakeholders in incident response? My interest in tracing this overall theme through a varied range of sub-tasks produces three more specific research questions: RQ1. How can information exposure and attention be operationalized to highlight cognitive and perceptual processes? RQ2. How do people’s perception of risk communications from stakeholders vary in crisis? RQ3. How could a principled and scalable pipeline be designed to identify people’s cognitive and affective perceptions on Twitter? I took cues from the Protective Action Decision Model (Lindell and Perry 2012) and leveraged baselines in the literature to address these research questions. To address the first research question, I proposed a metric that conceptualized and operationalized the predecision process. The proposed metric was incorporated into a pipeline and applied to two real-word events to recommend messages that represent the shift of collective attention of those locally affected with a specialized focus on cognitive and perceptual processes. To address the second research question, I went beyond the perception of risks to include perceptions of risk communications by stakeholders. I performed an empirical study of the relation between risk communications by stakeholders and different kinds of public perceptions (Lindell and Perry 2012). To address the third research question, I proposed a future work to provide benchmark coding schemes, datasets and models to quantitatively identify information representing cognitive and perceptual processes. I will leverage existing benchmark datasets in the literature (Olteanu et al. 2014; Imran et al. 2016; Alam et al. 2018; Zahra et al. 2020; Rudra et al. 2017; Mazloom et al. 2018; Purohit et al. 2018) and coding schemes in qualitative studies (Trumbo et al. 2016; Demuth et al. 2018) and create benchmark classification models.

Abstract: Disaster response management has become an important area of research in recent years, with authorities spending more resources in the area. Infrastructure resource interdependencies are key critical points for a system to operate optimally. After a disaster occurs, infrastructures would have sustained certain degrees of damage, the allocation of limited resources to maximize human survival becomes a top priority. The I2Sim (Infrastructures Interdependencies Simulator) research group at the University of British Columbia (UBC) has developed a software simulation toolbox to help authorities plan for disaster responses. This paper presents an optimization decision algorithm based on Lagrange multipliers, which provides the theoretical basis for I2Sim software decision maker layer. There is a simple scenario of three hospitals constructed with the I2Sim toolbox to illustrate the interdependencies of water and electricity. © 2012 ISCRAM.

Abstract: Automated coordination is regarded as a novel approaches in Emergency Response Systems (ERS), and especially resource allocation has been understudied in former research. The contribution of this paper is the introduction of two variants of a novel resource allocation mechanism that provide decision support to the centralized Emergency Operations Center (EOC). Two quantitative models are computationally validated using real-time, data-driven, Monte-Carlo simulations promoting reliable propositions of distributed resource allocations and schedules. Various requirements are derived through a literature analysis. Comparative analyses attest that the Monte-Carlo approach outperforms a well-defined benchmark.

Abstract: In an extreme event or major disaster, very often there are both alternative actions that might be considered and far more requests for actions than can be executed immediately. The relative desirability of each option for action could be a collaborative expression of a significant number of emergency managers and experts trying to manage the most desirable alternatives at any given time, in real time. Delphi characteristics can satisfy these needs given that anyone can vote or change their vote on any two options, and voting and scaling are used to promote a group understanding. Further utilized with Thurstone's Law of Comparative Judgment, a group decision or the range of acceptability a group is willing to consent to, can be calculated and utilized as a means of producing the best decision. A ubiquitous system for expeditious real-time decision making by large virtual teams in emergency response environments is described.

Abstract: This paper describes some of the lessons learned from the FireGrid project. It starts with a brief overview of the project. The discussion of the lessons learned that follows is intended for others attempting to develop a similar system, where sensor data is used to steer a super-real time simulation in order to generate predictions that will provide decision support for emergency responders.

Abstract: In this paper we describe a work-in-progress decision support program designed for use in the response to major incidents in the UK. The proposed program is designed for use in a continuous fashion, where the updating of its model, the search for solutions to the model through an optimization algorithm, and the issuing of these solutions are carried out concurrently. The model facilitates the inclusion of dynamic and uncertain features of emergency response. The potential of such an approach to deliver high-quality response plans through enabling more accurate modeling is evaluated through focusing on the case of casualty health information. Computational experiments show there is significant value in monitoring the dynamic and uncertain health progression of casualties and updating the model accordingly. © 2012 ISCRAM.

Abstract: The Decision Support System (DSS) Emergency Planning is designed for use in the event of sea or river flooding. It makes accessible all the information related to the decision whether to evacuate an area. An important factor in this decision is the time required for the evacuation. The model used by the DSS Emergency Planning system to estimate the time required employs a strategy that prevents congestion on the road network in the area at risk. The use of the DSS Emergency Planning system during the proactive and prevention phases enables disaster containment organisations to prepare better for a flood situation. Moreover, all relevant information is saved and is therefore available for the post-disaster evaluation. The DSS Emergency Planning system can play a significant role in ensuring that the evacuation of an area at risk goes according to plan. In the future the DSS Emergency Planning system can also be used to evacuate people in the event of a nuclear, natural fire or extreme weather disaster.

Abstract: Effective decision-supporting visualization is critical for strategic, tactic, and operational management before and during a large-scale climate or extreme weather emergency. Most emergency management applications traditionally consist of map-based event and object visualization and management, which is necessary for operations, but has small contribution to decision makers. At the same time, analytical models and simulations that usually enable prediction and situation evaluation are often analyst-oriented and detached from the operational command and control system. Nevertheless, emergencies tend to generate unpredictable effects, which may require new decision-support tools in real-time, based on alternative data sources or data streams. In this paper, we advocate the use of dashboards for emergency management, but more importantly, we propose an intelligent mechanism to support effective and efficient utilization of data and information for decision-making via flexible deployment and visualization of data streams and metric displays. We employ this framework in the H2020 beAWARE project that aims to develop and demonstrate an innovative framework for enhanced decision support and management services in extreme weather climate events.

Abstract: In the 21st century, rapid changes of our society necessitate continuous review and practice of emergency plans. Traditional face-to-face (FtF) interactions to make emergency plans and train responders seem insufficient. The virtual team (VT), a new team form allowing dynamic recruitment of experts from global extent and conduction of teamwork whenever it is needed, provides a more agile solution. This paper introduces a group support system called Collario (Collaborative Scenario) aiming to facilitate effective collaboration in creating and discussing scenarios in VTs and to utilize scenarios as the vehicle to review and practice emergency plans on a continuous basis. This research is still in progress. Three professionals have been involved in system demonstrations and interviews. Although it is still too early to make any conclusions, it is encouraging to know that all the three experts thought Collario easy to use and might be useful for various emergency preparedness purposes.

Abstract: We have been using exploratory modeling to forecast multiple plausible outcomes for a set of decision options situated in the emergency response domain. Results were displayed as a set of box-plots illustrating outcome frequencies distributed across an evaluative dimension (e.g., cost, score, or utility). Our previous research showed that such displays provide what we termed “option awareness” – an ability to determine robust options that will have good outcomes across the broadest number of plausible futures. This paper describes an investigation into extending this approach to collaborative decision making by providing a visualization of both collaborative and individual decision spaces. We believe that providing such visualizations will be particularly important when each individuals decision space does not account for the synergy that may emerge from collaboration. We describe how providing collaborative decision spaces improves the robustness of joint decisions and engenders high confidence in these decisions.

Abstract: The dynamic nature of emergency decision making exerts difficulty to decision makers for achieving effective management. In this regard, we suggest a dynamic decision making model based on Markov decision process. Our model copes with the dynamic decision problems quantitatively and computationally, and has powerful expression ability to model the emergency decision problems. We use a wildfire scenario to demonstrate the implementation of the model, as well as the solution to the firefighting problem. The advantages of our model in emergency management domain are discussed and concluded in the last.