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Christoph Aubrecht, Sérgio Freire, Josef Fröhlich, Beatrice Rath, & Klaus Steinnocher. (2011). Integrating the concepts of foresight and prediction for improved disaster risk management. In E. Portela L. S. M.A. Santos (Ed.), 8th International Conference on Information Systems for Crisis Response and Management: From Early-Warning Systems to Preparedness and Training, ISCRAM 2011. Lisbon: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: This discussion paper focuses on conceptualizing the ultimate goal in disaster management, i.e. reduction of future risks and impacts and explicitly highlights how actions taken in various phases of integrated disaster risk management influence vulnerability and eventually overall risk characteristics. First, the advancement of the disaster management concept evolving from a cyclic perspective to a spiral view is described and the various stages of disaster management including risk analysis, mitigation, and response are explained. In an attempt to improve and advance disaster risk management, next, the concepts of foresight and prediction are described and its major differences are highlighted. Finally, the basic framework of risk governance is considered for integrating foresight and prediction and thus lifting disaster management to the next level. Active and transparent communication and participation is seen as the key for successfully implementing risk governance.
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Cruz, J. A. dela, Hendrickx, I., & Larson, M. (2023). Towards XAI for Information Extraction on Online Media Data for Disaster Risk Management. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 478–486). Omaha, USA: University of Nebraska at Omaha.
Abstract: Disaster risk management practitioners have the responsibility to make decisions at every phase of the disaster risk management cycle: mitigation, preparedness, response and recovery. The decisions they make affect human life. In this paper, we consider the current state of the use of AI in information extraction (IE) for disaster risk management (DRM), which makes it possible to leverage disaster information in social media. We consolidate the challenges and concerns of using AI for DRM into three main areas: limitations of DRM data, limitations of AI modeling and DRM domain-specific concerns, i.e., bias, privacy and security, transparency and accountability, and hype and inflated expectations. Then, we present a systematic discussion of how explainable AI (XAI) can address the challenges and concerns of using AI for IE in DRM.
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Monika Buscher, Maria Alejandra Lujan Escalante, Hayley Alter, & Xaroula Kerasidou. (2018). Is-IT-ethical? Responsible Research and Innovation for Disaster Risk Management. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 254–267). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Even though there are approaches for responsible research and innovation and recommendations on how to use IT, there is a lack of standardisation and guidance that integrates the perspectives of researchers, technology developers, end users, and society at large. In this paper we describe is-IT-ethical?, a European knowledge and service hub we are developing for diverse parties involved in crisis and disaster risk management with a commitment to European values and fundamental rights. The overarching rationale of the initiative is to support European societies' need to effectively balance the benefits of IT with values of democracy and fundamental rights, especially privacy and data protection. The initiative builds on more than seven years of research with practitioners, academic, and commercial IT designers. This paper describes our motivations and a prototype.
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Oscar Durán, Catalina Esquivel, & Edward Ruiz. (2015). Sizing the Infrastructure and Architecture of Information for Risk Management. In L. Palen, M. Buscher, T. Comes, & A. Hughes (Eds.), ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management. Kristiansand, Norway: University of Agder (UiA).
Abstract: In Costa Rica, there is an acceptable work in the area of risk management and an advanced system of emergency response. However, it is recognized and accepted the lack of a comprehensive shared approach to manage disaster risk that involves the prevention of disasters in the National System for Disaster Risk Management (SNGR in Spanish). One of the main needs is the lack of a shared and accessible national platform of timely and updated information for risk management. Considering this weakness, we submitted a proposal to the authorities of the University of Costa Rica (UCR) and the Consejo Superior Universitario Centroamericano (CSUCA) that was sponsored by the Swiss Agency for Development and Cooperation (SDC). The project seek to develop an information platform which uses a Content Management System with meta data, semantic , taxonomic and georeferenced information for local, regional and national levels in Costa Rica. The system also serves as a network for data producers, analysts, users of public and private institutions, and of the population in general.
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Oscar Durán, Edward Ruiz, & Catalina Esquivel. (2016). Towards a Monitoring and Follow Up System for the Costa Rican Risk Management System. In A. Tapia, P. Antunes, V.A. Bañuls, K. Moore, & J. Porto (Eds.), ISCRAM 2016 Conference Proceedings ? 13th International Conference on Information Systems for Crisis Response and Management. Rio de Janeiro, Brasil: Federal University of Rio de Janeiro.
Abstract: The institutions that make up the Costa Rican Risk Management System are in charge of implementing the National Policy on Risk Management. It is necessary to highlight the scope of each institution regarding such implementation. This paper presents the proposal for a monitoring and follow up model. The monitoring to be implemented must process and provide precise information for decision making. This work presents the conceptual and methodological aspects for the aforementioned monitoring system.
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Susanne Kubisch, Johanna Stötzer, Sina Keller, María Bull, & Andreas Braun. (2019). Combining a social science approach and GIS-based simulation to analyse evacuation in natural disasters: A case study in the Chilean community of Talcahuano. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management. Valencia, Spain: Iscram.
Abstract: In rapid-onset disasters the time needed for evacuation is crucial. Aside from the behaviour of the population, the
road network plays a fundamental role. It serves as a medium to reach a safe area. This study analyses the entire
evacuation process, from decision-making up to the arrival at an evacuation zone by combining standardised
questionnaires and GIS-based simulation. Based on a case study in the Chilean community of Talcahuano, an
event-based past scenario and a hypothetical future scenario is investigated, integrating the affected population in
the research process. The main problem identified in past evacuations has been time delay due to congestions,
which also is evident in the results of the hypothetical future scenario. A result which supports evacuation by foot.
This paper argues that a combination of scientific methods is essential for analysing evacuation and to reduce the
risk due to time delay, critical route and transport medium choice.
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Valerio Lorini, Carlos Castillo, Francesco Dottori, Milan Kalas, Domenico Nappo, & Peter Salamon. (2019). Integrating Social Media into a Pan-European Flood Awareness System: A Multilingual Approach. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management. Valencia, Spain: Iscram.
Abstract: This paper describes a prototype system that integrates social media analysis into the European Flood Awareness
System (EFAS). This integration allows the collection of social media data to be automatically triggered by flood
risk warnings determined by a hydro-meteorological model. Then, we adopt a multi-lingual approach to find
flood-related messages by employing two state-of-the-art methodologies: language-agnostic word embeddings
and language-aligned word embeddings. Both approaches can be used to bootstrap a classifier of social media
messages for a new language with little or no labeled data. Finally, we describe a method for selecting relevant and
representative messages and displaying them back in the interface of EFAS.
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