-Nuclear emergencies are characterised by severe disruptions in society's functionality and adverse impacts on human, environment and economy. Decision-making in times of such crises is complex and usually accompanied by acute time pressure. Environment can change rapidly and decisions may have to be made based on uncertain information. IT-based decision support can systematically help to identify response and recovery measures, especially when time for decision-making is sparse, when numerous options exist or when events are not completely anticipated. This paper reviews the case-and scenario-based approach implemented in the so-called analytical platform to support the management of nuclear events in different accident phases. Important information needed for decision-making as well as approaches to reusing experience from previous events and the fictitious scenarios calculated by Java version of the Real-time On-line Decision Support System are discussed. Suitable management options based on similar historic events and scenarios might be identified to support disaster management.
-The European project PREPARE (innovative integrated tools and platforms for radiological emergency preparedness and post-accident response in Europe) aims at closing gaps that have been identified in nuclear and radiological preparedness following the first evaluation of the Fukushima disaster. Among others, a work package was established to develop a so-called analytical platform (AP) exploring the scientific and operational means to improve information collection, information exchange and the evaluation of such types of disasters. The AP contains several modules supporting the work of experts in analysing an ongoing event and in communicating with the public.
Systems of critical infrastructures are characterized by strong interdependencies and the developments of urban areas towards Smart Cities even increase the underlying complexity due to growing automation and interconnectedness. A system of highly cross-linked components is especially prone to systemic risks making concepts of resilience accordingly important. One way for being able to withstand in times of stress, maintain security of supply, and promote adaptive and anticipative capabilities, is to establish early warning capabilities. As cities are complex and rather chaotic socio-technical systems reigned by randomness, the caused parametric uncertainties challenge modeling approaches that are intended to support robust decision-making. Sophisticated methods based on artificial intelligence can play an essential role in this case, as they perform well on highly complex environments and large data set. To study resilience, the urban area is split into zones where the city's state is determined by the states of these zones and the state of a zone is characterized by the criticalities of infrastructures accommodated there. Considering criticality as an atomic building block for urban performance assessments, this paper proposes a zone-based state forecast methodology by applying deep convolutional neural networks for learning state evolution that is influenced by non-linear demand dynamics. Furthermore, a case study is presented that applies agent-based simulations and underlines the relevance of deep learning approaches for Smart City early warning systems.
In the context of the energy transition, sound decision making regarding the development of renewable energy systems faces various technical and societal challenges. In addition to climate-related uncertainties affecting technical issues of reliable grid planning, there are also subtle aspects and uncertainties related to the integration of energy technologies into built environments. Citizens’ opinions on grid development may be ambiguous or divergent in terms of broad acceptance of the energy transition in general, and they may have negative attitudes towards concrete planning in their local environment. First, this article identifies the issue of discrepancies between preferences of a fixed stakeholder group with respect to the question of the integration of renewable energy technology, posed from different perspectives and at different points in time, and considers it as a fundamental problem in the context of robust decision making in sustainable energy system planning. Second, for dealing with that issue, a novel dynamic decision support methodology is presented that includes multiple surveys, statistical analysis of the discrepancies that may arise, and multicriteria decision analysis that specifically incorporates the opinions of citizens. Citizens are considered as stakeholders and participants in smart decision-making processes. A case study applying agent-based simulations underlines the relevance of the methodology proposed for decision making in the context of renewable energies.
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