Abstract: Social media can inform response agencies during disasters to help affected people. However, filtering informative messages from social media content is challenging due to the ungrammatical text, out-of-vocabulary words, etc., that limit the context interpretation of messages. Further, there has been limited exploration of the challenge of code-mixing (using words from another language in a given text of one language) in user-generated content during disasters. Hence, we proposed a new code-mixed dataset of tweets related to the 2017 Iran-Iraq Earthquake and annotated them based on their informativeness characteristics. Additionally, we have evaluated the performance of state-of-the-art pre-trained language models: mBERT, RoBERTa, and XLM-R, on the proposed dataset. The results show that mBERT (with F1 score of 72%) overweighs the other models in classifying informative code-mixed messages. Moreover, we analyzed some patterns of exploiting code-mixing by users, which can help future works in developing these models.

Abstract: In the field of public transport, operators and first responders collaborate in the prevention of and reaction to security issues. In order to optimise their specific daily operational business needs in a timely manner heterogeneous information and communication systems are deployed. In case of an incident however it is crucial that the various involved parties exchange relevant information to develop a shared understanding and act in a coordinated way. Yet, heterogeneous communication and information system infrastructures often hinder this crucial flow of information. To address this shortcoming it is crucial to enable the design of interoperable system-of-systems approaches in this domain. This paper describes a conceptual model to construct system-of-systems environments in the domain of security in public transport. By building on the results of several European research projects this concept offers a starting point for modelling and documenting individual systems inside a system-of-systems architecture. © 2012 ISCRAM.

Abstract: Man-made and natural disasters have affected people worldwide. Mass casualty incidents would create a surge in demand for medical services. Medical service needs are the basis of medical strategic readiness plan. In recent years, international actions have been criticized for being ill-adapted to dominating health needs of the affected region. The “Scenario-Response” modeling is an important method in disaster prediction. This research established a medical service needs scenario model with two different levels of ambition: a disaster scenario, in which casualty figure, composition of injuries are constrained by the types of the disaster as well as the degree of the damage, and a country scenario, in which the healthcare needs are constrained by the health coverage and the health condition of local people. Armed conflicts in Yemen and Syria Arab Republic were analyzed by this model. The results showed that the outcome of this model fit the reality.

Abstract: Since the beginning of this millennium, there has been an increasing use of social media and crowdsourcing (SMCS) technologies in disaster situations (Reuter & Kaufhold, 2018). Disaster management organizations and corresponding research are increasingly working on ways of integrating SMCS into the processes of crisis management. In a changing technological landscape to address disasters, and with increasing diversity of stakeholders in disasters, the purpose of this research is to provide an overview of technologies for SMCS within disasters to improve community resilience. The identified and analyzed technologies are summarized under the term “Disaster Community Technologies” (DCT). The paper presents a classification schema (the “DCT-schema”) for those technologies. The goal is to generate an overview of DCT in a rapidly evolving environment and to provide the practical benefit for different stakeholders to identify the right one from the overview.

Abstract: Common denominators emerge difficultly in projects bridging science and society or/and across disciplines. Managing crises require inter-organizational learning and citizen involvement, but, often such undertakings lead to bargain resulting in sub-optimal decisions. Building resilience into human communities demands complex projects, which further require good problem definition, starting with agreements on values and knowledge, as basis for further agreements on goals and methods. This paper spreads the Data-Information-Knowledge-Action-Result frame over a 4-level process to generate a DIKAR_process matrix and lattice that allows optimal orientation and coordination towards achieving a set of common denominators and coordinated action protocols. This framework allows sequences and cycles that can be formulated and pursued simultaneously, comparatively and iteratively, within any large, heterogeneous constituency of actors involved in building resilience in local communities. The model is illustrated and discussed in relation to urban sustainability issues and complementary methods like knowledge maps, mental models, social learning and scenarios.

Abstract: Building up a proper understanding of a large-scale incident is an important and difficult process. We envision a working environment for decision makers in crisis management situations that allows them to work with information in various ways. That will stimulate them to think critically in processing the information they receive-All in support of rapid sensemaking and decision making. To realize this ambition, we combine various technologies into an integrated support concept called the Critical Thinking Environment (CTE), aimed at tackling critical issues in sensemaking.

Abstract: In addition to their normal task of supporting community participation, engagement, and improved information access, information technology-based public service systems are also essential for maintaining critical services and providing effective communication with citizens before, during, and after emergencies. This study focuses on the impacts of disaster events on the operational performance of such service systems and discusses opportunities for managing service efficiency by rearranging and reallocating resources during emergencies. To the best of our knowledge, this is the first attempt to provide a practical method for improving the relative efficiency of public service systems in such a context. We suggest a Data Envelopment Analysis (DEA) approach for quantifying the relative efficiencies associated with service requests from an input-output-based standpoint, and discuss the Orange County (Florida) 311 non-emergency service system, in the context of the COVID-19 pandemic, as an example of how such operational efficiency can be managed during a disruption.

Abstract: Optimal communication and information exchange are key elements for handling complex crises or disaster situations. With the increasing number of heterogeneous ICT systems, also raises the importance of adequate support for interconnectivity and information logistics between stakeholders to thoroughly gather information and to make quick but precise decisions. The main purpose of the information exchange is then to manage the crisis as quickly as possible, to provide full information to protect first responders' health and safety, to optimally dispatch resources, and to ensure coordination between different relief forces. Based on an end user survey with a particular focus on first responders, this paper introduces an evolutionary architecture to enable information exchange in crises situation or disasters. The aim is to provide a decentralized approach among heterogeneous ICT-systems which abstracts from the underlying communication technologies and heterogeneity of connected systems and fulfills the functional and non-functional requirements from end users.

Abstract: Often, water management decisions are based on hydrological forecasts, which are affected by inherent uncertainties. It is increasingly common for forecasters to make explicit estimates of these uncertainties. Associated benefits include the decision makers' increased awareness of forecasting uncertainties and the potential for risk-based decision-making. Also, a more strict separation of responsibilities between forecasters and decision maker can be made. A recent study identified some issues related to the effective use of probability forecasts. These add a dimension to an already multi-dimensional problem, making it increasingly difficult for decision makers to extract relevant information from a forecast. Secondly, while probability forecasts provide a necessary ingredient for risk-based decision making, other ingredients may not be fully known, including estimates of flood damage and costs and effect of damage reducing measures. Here, we present suggestions for resolving these issues and the integration of those solutions in a prototype decision support system (DSS). A pathway for further development is outlined.

Abstract: This article shows the research process carried out by Regional Government of southern Europe, with more than 8 million citizens, to create an Information System to serve as a diagnostic and certification model for the resilience level of the municipalities of that region. This Information System will allow the local authorities of the regional governments to know in what situation they are and what they should do to improve their resilience level. The research framework is based on the best practices in urban resilience. One of the relevant characteristics of the work is the integration of the knowledge of a very heterogeneous group of experts for the identification of the special needs of the target region that has been articulated through a Delphi process.

Abstract: This paper presents the results of our research on the development of a Dialog Action Manager-DAM as part of a complex crisis management system. Imagine the utility of such an automatic system to detect the crisis and to provide support to people in contexts similar to what happened recently at the underground in London and Madrid. Preventing and handling the scenarios of terrorism and other crisis are nowadays maybe the most important issues for a modern and safe society. In order to automate the crisis support, DAM simulates the behavior of an employee at the crisis centre handling telephone calls from human observers. Firstly, the system has to mimic the natural support for the paradigm 'do you hear me?' and next for the paradigm 'do you understand me?'. The people witnessing the crisis event as well as human experts provide reports and expertise according to their observations and knowledge on the crisis. The system knowledge and the data communication follow the XML format specifications. The system is centered on the results of our previous work on creating a user-centered multimodal reporting tool that works on mobile devices. In our paper we describe the mechanisms for creating an automatic DAM system that is able to analyze the user messages, to identify and track the crisis contexts, to support dialogs for crisis information disambiguation and to generate feedback in the form of advice to the users. The reasoning is done by using a data frame and rule based system architecture and an alternative Bayesian Network approach. In the paper we also present a series of experiments we have attempted in our endeavor to correctly identify natural solutions for the crisis situations.

Abstract: Technological advances in both distributed cooperative work and web-map services have the potential to support distributed and collaborative time-critical decision-making for crisis response. We address this potential through the theoretical perspective of distributed cognition and apply this perspective to development of a geocollaborationenabled web application that supports coordinated crisis management activities. An underlying goal of our overall research program is to understand how distributed cognition operates across groups working to develop both awareness of the geographic situation within which events unfold, and insights about the processes that have lead to that geographic situation over time. In this paper, we present our preliminary research on a web application that addresses these issues. Specifically, the application (key parts of which are implemented) enables online, asynchronous, map-based interaction between actors, thus supporting distributed spatial and temporal cognition, and, more specifically, situational awareness and subsequent action in the context of humanitarian disaster relief efforts.