Nada Matta, Paul Henri Richard, Alain Hugerot, & Theo Lebert. (2022). Experience Feedback Capitalization of Covid-19 Management in Troyes city. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 311–319). Tarbes, France.
Abstract: All countries have to face the COVID’19 pandemic and its heavy consequences. This sanitary crisis differs from all others in terms of the quick spread of contaminations, the high number of deaths (more than 5,5 Million globally and 123,893 in France) and the accrued number of patients hospitalized and induced in intensive care units. All sanitary procedures have proven to be inadequate. Several actors at different levels, whether international, European, national and local, as well as at the level of public and private organizations have been involved in the management of this type of crisis. These actors deal with different aspects of it, i.e., health, people protection, and economic and social situations. Existing procedures revealed a big lack in the relationships between different local and departmental actors. We did a number of interviews with strategic actors addressing the COVID’19 crisis in the City of Troyes. The objective of these interviews is to identify lessons learned from their experience feedback about relational problems and modifications needed. We present in this paper the first results of this study.
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Koki Asami, Shono Fujita, Kei Hiroi, & Michinori Hatayama. (2022). Data Augmentation with Synthesized Damaged Roof Images Generated by GAN. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 256–265). Tarbes, France.
Abstract: The lack of availability of large and diverse labeled datasets is one of the most critical issues in the use of machine learning in disaster prevention. Natural disasters are rare occurrences, which makes it difficult to collect sufficient disaster data for training machine learning models. The imbalance between disaster and non-disaster data affects the performance of machine learning algorithms. This study proposes a generative adversarial network (GAN)- based data augmentation, which generates realistic synthesized disaster data to expand the disaster dataset. The effect of the proposed augmentation was validated in the roof damage rate classification task, which improved the recall score by 11.4% on average for classes with small raw data and a high ratio of conventional augmentations such as rotation of image, and the overall recall score improved by 3.9%.
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Christian Iasio, Ingrid Canovas, Elie Chevillot-Miot, & Tendry Randramialala. (2022). A New Approach to Structured Processing of Feedback for Discovering and Investigating Interconnections, Cascading Events and Disaster Chains. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 285–298). Tarbes, France.
Abstract: Post-disaster information processing is relevant for the continuous improvement of operations and the reductionof risks. The current methodologies for post-disaster review suffer from several limitations, which reduce their use as a way of translating narrative in data for qualitative and quantitative analysis. Learning or effective knowledge sharing need a common formalism and method. Ontologies are the reference tool for structuring information in a “coded” data structure. Using the investigation of disaster management during the 2017 hurricane season in the French West Indies within the scope of the ANR “APRIL” project, this contribution introduces a methodology and a tool for providing a graphical representation of experiences for post-disaster review and lessons learning, based on a novel approach to case-based ontology development.
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Cendrella Chahine, Thierry Vidal, Mohamad El Falou, & François Pérès. (2022). Multi-Agent Dynamic Planning Architectures for Crisis Rescue Plans. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 243–255). Tarbes, France.
Abstract: We are interested in rescue management in crises such as in terrorist attacks. Today, there are emergency plans that take into account all the stakeholders involved in a crisis depending on the event type, magnitude and place. Unfortunately, they do not anticipate the evolution of the crisis situation such as traffic and hospital overcrowding. In addition, decisions are taken after the information has been passed from the operational level to higher levels. This work focuses on the operational level of the emergency plan. What will happen if the actors at this level, can make certain decisions without escalating the information to higher levels? To answer this question, a multi-agent dynamic planning approach is proposed and it will be tested in two different architectures in order to see how much autonomy can be given to an agent and how they coordinate to save the victims.
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Aïdin Sumic, Emna Amdouni, Thierry Vidal, & Hedi Karray. (2022). Towards Flexibility Sharing in Multi-agent Dynamic Planning: The Case of the Health Crisis. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 274–284). Tarbes, France.
Abstract: Planning problems in a crisis context are a highly uncertain environment where health facilities must cooperate in providing health services to their patients. We focus on the health crisis in France due to the COVID19 pandemic. In fact, the lack of appropriate scheduling tools, resources, and communication leads hospitals to be submerged by infected patients and forced to transfer them to other hospitals. In this work we aim to provide a global solution to such planning problems to improve the current French health system. We introduce a cooperative approach called OPPIC (Operational Planning Platform for Inter-healthcare Coordination). OPPIC is based on a decentralized system, where health facilities plan is dynamic, flexible, robust to uncertainty, and respond to goals and optimization criteria. This paper proposed a first planning model to OPPIC and provided a first way of negotiation between health facilities based on their plan’s local and global flexibility.
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