Disaster Information, Resilience, for Emergency and Crisis Technologies

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    Which Factors Govern the Use of Emergency Response Information Systems? Insights from an Ethnographical Study of a Voluntary Fire Department
    ( 2022-01-04) Weidinger, Julian ; Schlauderer, Sebastian ; Overhage, Sven
    To realize the digitalization potential of emergency response processes, several information technologies have been proposed that shall support firefighters in their operations. In the incident command process, especially emergency response information systems (ERIS) are supposed to raise the situation awareness and overall efficacy. Despite their theoretical potential, these technologies only slowly disseminate in practice, however. While extant acceptance models can basically explain firefighters’ intention to use them, the actual usage so far remained unexplored. To gain an in-depth understanding of the specific domain and its influence on the usage of technologies, we ethnographically observed a voluntary fire department over several years. During its digitalization of command processes, we identified operational specialties like flexibility, organizational requirements like error culture, and social aspects like perceived importance that influence the introduction of an ERIS. These factors shall enrich existing acceptance models and help to better consider the special characteristics of the firefighter domain.
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    Using a Public Safety Radio Network for Information Negotiation between the Three-Tiered Command and Control Structure
    ( 2022-01-04) Steen-Tveit, Kristine
    Multi-organizational emergency operations require effective information sharing. Existing information management tools supporting a common operational picture mainly convey factual information. However, a growing body of literature recognizes the importance of sharing interpretations and implications among the involved stakeholders for building a common situational understanding. This study aims to identify information that must be negotiated across the strategic, tactical, and operational command and control structures (C2S) for developing common situational understanding. Based on 33 interviews and a survey of emergency management stakeholders, information elements on the semantic and pragmatic levels are identified. Further, the results suggest how to use a secure radio network for facilitating information sharing so that the involved organizations can monitor and negotiate important information. These insights provide important lessons for improving information sharing in the emergency management domain.
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    Towards Digital Transformation of a City Resilience Framework
    ( 2022-01-04) Canós, José ; Penadés, M. Carmen ; Borges, Marcos R. S. ; Bueno, Salvador ; Hernantes, Josune ; Labaka, Leire ; Bañuls Silvera, Víctor
    Improving city resilience is among the most challenging strategic goals for city administrators worldwide. To support their work, frameworks providing technical support and methodological guidance have been developed. Such frameworks define resilience improvement processes based on multidimensional resilience models to assess one city’s resilience level, plus a collection of policies to increase such level in different dimensions. Although some frameworks include software tools to support the process, their scope is limited to a particular step of the process, and global management is still done manually, hindering agility in the process. In this paper, we present our work towards the digital transformation of a city resilience framework. The use of process technology to specify and enact the process is combined with the application of model-based development techniques to provide interoperability of the different framework tools. We describe the architecture of the solution proposed, and the major features of our approach.
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    Persistent physics-based crisis management framework: A case study of traffic in the Nantes city due to flood exposure
    ( 2022-01-04) Moradkhani, Nafe ; Dolidon, Hélène ; Benaben, Frederick
    In the context of crisis, the characteristics of the crisis area and the operational measures of the community play key roles in managing the crisis. The Nantes ring road in France is always exposed to flooding and its disruptions. To anticipate the disruptions and timely preventive actions for this frequent phenomenon, the main challenges are (i) forecast of vehicles' flows, (ii) capacity of the ring road to handle the traffic (iii) evaluate the performance of alternate routes during the flooding. The flooded area as a system has components of (i) the flood (e.g. time of onset, magnitude, intensity, etc.), (ii) the area (e.g. geographical features, temporary perimeter barriers, dam, diversion canals), and (iii) the community (e.g. reaction time, emergency strain, evacuation delay). The approach chosen to conduct this anticipative study consists of collecting data about forecasts and using simulation models to work simultaneously on evaluating the performance of the ring road and its alternative routes.
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    Modeling renewable energy production and CO2 emissions in the region of Adrar in Algeria using LSTM neural networks
    ( 2022-01-04) Bouziane, Seif Eddine ; Dugdale, Julie ; Khadir, Mohamed Tarek
    This paper addresses the slow-onset crisis of global warming caused by CO2 emissions. Although electrical load is a major influence in a country’s growth and development, it is also one of largest sources of greenhouse gases (GHG), CO2 in particular. Therefore, switching to cleaner energy sources is a clear objective and forecasting electricity load and its environmental cost is a necessary task for electrical energy planning and management. This paper addresses short-term load forecasting of renewable energy (RE) production in the region of Adrar in Algeria with Adrar’s photovoltaic (PV) farm and Kabertene’s wind farm. The forecast is compared to the overall load demand, and the reduced amount of CO2 resulting from using renewable energy instead of fossil fuels is calculated. The forecasting models are Long short-term memory (LSTM) neural networks, which were trained and validated using real data provided by the national state-owned company SONALGAZ. The results show good performance for the forecasting models with PV and wind models achieving a Mean-absolute-error (MAE) of 0.024 and 0.1 respectively, and that RE can help reduce CO2 emissions by up to 25% per hour.