Florent Dubois, Paul Renaud-Goud, & Patricia Stolf. (2022). Dynamic Capacitated Vehicle Routing Problem for Flash Flood Victim’s Relief Operations. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 68–86). Tarbes, France.
Abstract: Flooding relief operations are Dynamic Vehicle Routing Problems (DVRPs). The problem of people evacuation is addressed and formalized in this paper. Characteristics of this DVRP problem applied to the crisis management context and to the requirements of the rescue teams are explained. In this paper, several heuristics are developed and assessed in terms of performance. Two heuristics are presented and adapted to the dynamic problem in a re-optimization approach. An insertion heuristic that inserts demands in the existing plan is also proposed. The evaluation is conducted on various dynamic scenarios with characteristics based on a study case. It reveals better performances for the heuristics with a re-optimization approach.
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Stella van Esch, Marc van den Homberg, & Kees Boersma. (2021). Looking Beyond the Data: an Assessment of the Emerging Data Ecosystem of Nepal's Flood Early Warning Systems. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 282–293). Blacksburg, VA (USA): Virginia Tech.
Abstract: Increasingly, data-driven instruments are used in disaster risk reduction to foster more efficient, effective, and evidence-based decision-making. This data revolution brings along opportunities and challenges, which are sometimes related to the data itself, but more often seem related to the environment in which the data is put to use. To provide insight into such an emerging data ecosystem, this paper uses a qualitative case study to assess the use of data in flood early warning systems (EWS) in Nepal. In response to the research question 'How does the data ecosystem impact the opportunities and challenges regarding data use in flood early warning systems in Nepal?', this paper discusses the importance of considering the broader context instead of regarding data as an entity unto itself. It shows how actors, policies and other contextual factors impact the effectiveness of data use by either presenting opportunities, like the establishment of a national disaster data repository, or challenges, like inadequate human resources for working with data.
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Giulio Palomba, Alessandro Farasin, & Claudio Rossi. (2020). Sentinel-1 Flood Delineation with Supervised Machine Learning. In Amanda Hughes, Fiona McNeill, & Christopher W. Zobel (Eds.), ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management (pp. 1072–1083). Blacksburg, VA (USA): Virginia Tech.
Abstract: Floods are one of the major natural hazards in terms of affected people and economic damages. The increasing and often uncontrolled urban sprawl together with climate change effects will make future floods more frequent and impacting. An accurate flood mapping is of paramount importance in order to update hazard and risk maps and to plan prevention measures. In this paper, we propose the use of a supervised machine learning approach for flood delineation from satellite data. We train and evaluate the proposed algorithm using Sentinel-1 acquisition and certified flood delineation maps produced by the Copernicus Emergency Management Service across different geographical regions in Europe, achieving increased performances against previously proposed supervised machine learning approaches for flood mapping.
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Anastasia Moumtzidou, Marios Bakratsas, Stelios Andreadis, Anastasios Karakostas, Ilias Gialampoukidis, Stefanos Vrochidis, et al. (2020). Flood detection with Sentinel-2 satellite images in crisis management systems. In Amanda Hughes, Fiona McNeill, & Christopher W. Zobel (Eds.), ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management (pp. 1049–1059). Blacksburg, VA (USA): Virginia Tech.
Abstract: The increasing amount of falling rain may cause several problems especially in urban areas, which drainage system can often not handle this large amount in a short time. Confirming a flooded scene in a timely manner can help the authorities to take further actions to counter the crisis event or to get prepared for future relevant incidents. This paper studies the detection of flood events comparing two successive in time Sentinel-2 images, a method that can be extended for detecting floods in a time-series. For the flood detection, fine-tuned pre-trained Deep Convolutional Neural Networks are used, testing as input different sets of three water sensitive satellite bands. The proposed approach is evaluated against different change detection baseline methods, based on remote sensing. Experiments showed that the proposed method with the augmentation technique applied, improved significantly the performance of the neural network, resulting to an F-Score of 62% compared to 22% of the traditional remote sensing techniques. The proposed method supports the crisis management authority to better estimate and evaluate the flood impact.
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Valerio Lorini, Javier Rando, Diego Saez-Trumper, & Carlos Castillo. (2020). Uneven Coverage of Natural Disasters in Wikipedia: The Case of Floods. In Amanda Hughes, Fiona McNeill, & Christopher W. Zobel (Eds.), ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management (pp. 688–703). Blacksburg, VA (USA): Virginia Tech.
Abstract: The usage of non-authoritative data for disaster management provides timely information that might not be available through other means. Wikipedia, a collaboratively-produced encyclopedia, includes in-depth information about many natural disasters, and its editors are particularly good at adding information in real-time as a crisis unfolds. In this study, we focus on the most comprehensive version of Wikipedia, the English one. Wikipedia offers good coverage of disasters, particularly those having a large number of fatalities. However, by performing automatic content analysis at a global scale, we also show how the coverage of floods in Wikipedia is skewed towards rich, English-speaking countries, in particular the US and Canada. We also note how coverage of floods in countries with the lowest income is substantially lower than the coverage of floods in middle-income countries. These results have implications for analysts and systems using Wikipedia as an information source about disasters.
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