Ahmed Laatabi, Benoit Gaudou, Chihab Hanachi, Patricia Stolf, & Sébastien Truptil. (2022). Coupling Agent-based Simulation with Optimization to Enhance Population Sheltering. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 116–132). Tarbes, France.
Abstract: Population sheltering is a recurrent problem in crisis management that requires addressing two aspects: evacuating vulnerable people using emergency vehicles and regulating movements of pedestrians and individual vehicles towards shelters. While these aspects have received considerable attention in modeling and simulation literature, very few approaches consider them simultaneously. In this paper, we argue that Agent-Based Modeling and Simulation (ABMS) and Optimization are two complementary approaches that can address the problem of sheltering globally and efficiently and be the basis of coherent frameworks for decision- and policy-making. Optimization can build efficient sheltering plans, and ABMS can explore what-if scenarios and use geospatial data to display results within a realistic environment. To illustrate the benefits of a framework based on this coupling approach, we simulate actual flash flood scenarios using real-world data from the city of Trèbes in South France. Local authorities may use the developed tools to plan and decide on sheltering strategies, notably, when and how to evacuate depending on available time and resources.
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Ahmed T. Elsergany, Amy L. Griffin, Paul Tranter, & Sameer Alam. (2015). Development of a Geographic Information System for Riverine Flood Disaster Evacuation in Canberra, Australia: Trip Generation and Distribution Modelling. 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: Given the importance of geographic information for riverine flood evacuations, a geographic information system (GIS) is a vital tool for supporting successful flood evacuation operations. This paper discusses the development of a GIS-based riverine flood evacuation model which used to model trip distributions between flooded areas and relocation shelters. As the ultimate goal of this research is to simulate, model, and optimise a planned evacuation, all components of evacuation time have been considered (e.g., travel time between flooded areas and relocation shelters, warning time for each flooded area, and the time needed for evacuation before these areas get inundated). As well, variation in population (static and dynamic population) within the flooded areas has been considered.
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Ana-Gabriela Núñez, Sebastián Cedillo, Andrés Alvarado Martínez, & Ma Carmen Penadés. (2020). Towards the Building of a Resilient City able to Face Flood Risk Scenarios. 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. 593–601). Blacksburg, VA (USA): Virginia Tech.
Abstract: Despite the efforts that have been made to inform the community about the possible environmental risks, there is still a general lack of information. Currently, we are working on a flood risk scenario focused on a proposal towards a resilient culture together with the support of Information Technologies (IT) as a way to manage information. The goal is twofold: (i) on the one hand, to manage data in a small scenario to analyze and process the data collected from sensors in different sites in a micro-basin. Data get from data processing such as flow and velocity will then be the input data for hydraulic models to predict floods downstream; (ii) on the other hand, to publicize the predictions and the data already processed means people can benefit from information on flood risks, and the different participants may change their perception and consider cooperating in improving resilience.
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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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Ben Ortiz, Laura Kahn, Marc Bosch, Philip Bogden, Viveca Pavon-Harr, Onur Savas, et al. (2020). Improving Community Resiliency and Emergency Response With Artificial Intelligence. 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. 35–41). Blacksburg, VA (USA): Virginia Tech.
Abstract: New crisis response and management approaches that incorporate the latest information technologies are essential in all phases of emergency preparedness and response, including the planning, response, recovery, and assessment phases. Accurate and timely information is as crucial as is rapid and coherent coordination among the responding organizations. We are working towards a multi-pronged emergency response tool that provide stakeholders timely access to comprehensive, relevant, and reliable information. The faster emergency personnel are able to analyze, disseminate and act on key information, the more effective and timelier their response will be and the greater the benefit to affected populations. Our tool consists of encoding multiple layers of open source geospatial data including flood risk location, road network strength, inundation maps that proxy inland flooding and computer vision semantic segmentation for estimating flooded areas and damaged infrastructure. These data layers are combined and used as input data for machine learning algorithms such as finding the best evacuation routes before, during and after an emergency or providing a list of available lodging for first responders in an impacted area for first. Even though our system could be used in a number of use cases where people are forced from one location to another, we demonstrate the feasibility of our system for the use case of Hurricane Florence in Lumberton, a town of 21,000 inhabitants that is 79 miles northwest of Wilmington, North Carolina.
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Carole Adam, & Eric Andonoff. (2019). Vigi Flood: a serious game for understanding the challenges of crisis communication. 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: Emergency managers receive communication training about the importance of being ?first, right and credible?,
which is not easy. For instance, in October 2018, the Aude department in the South-West of France was hit by
intense rain. Flash floods were hard to forecast and only the ?orange? level of vigilance could be raised initially, but
the population dismissed this very usual warning in that season. The ?red? level was then raised too late, leading
to high criticism. The main problem here is the loss of trust induced by too many ?false alarms?. In this paper
we propose a serious game called VigiFlood for raising awareness in the population about the difficulty of crisis
communication and their own responsibility for reacting to the alerts. The implemented game still has limited
functionality but already shows interesting results in helping the user to visualise and understand the trust dynamics
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Rui Chen, Thirumurugan Thiyagarajan, Raghav H. Rao, & JinKyu LeeK. (2010). Design of a FOSS system for flood disaster management. In C. Zobel B. T. S. French (Ed.), ISCRAM 2010 – 7th International Conference on Information Systems for Crisis Response and Management: Defining Crisis Management 3.0, Proceedings. Seattle, WA: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: In this paper we study how information technology solutions can be used when disasters strike. This research in progress focuses on flood disasters and it proposes the design for flood disaster management. To increase the utility of the disaster management information system, we follow the free and open source system (FOSS) concept. Informed by the management tasks of flood response, we elaborate the system requirements and key functionalities. The system has received preliminary evaluation by the domain experts and is currently under further development.
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Cheng Wang, Benjamin Bowes, Arash Tavakoli, Stephen Adams, Jonathan Goodall, & Peter Beling. (2020). Smart Stormwater Control Systems: A Reinforcement Learning Approach. 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. 2–13). Blacksburg, VA (USA): Virginia Tech.
Abstract: Flooding poses a significant and growing risk for many urban areas. Stormwater systems are typically used to control flooding, but are traditionally passive (i.e. have no controllable components). However, if stormwater systems are retrofitted with valves and pumps, policies for controlling them in real-time could be implemented to enhance system performance over a wider range of conditions than originally designed for. In this paper, we propose an autonomous, reinforcement learning (RL) based, stormwater control system that aims to minimize flooding during storms. With this approach, an optimal control policy can be learned by letting an RL agent interact with the system in response to received reward signals. In comparison with a set of static control rules, RL shows superior performance on a wide range of artificial storm events. This demonstrates RL's ability to learn control actions based on observation and interaction, a key benefit for dynamic and ever-changing urban areas.
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Shideh Dashti, Leysia Palen, Mehdi P. Heris, Kenneth M. Anderson, T. Jennings Anderson, & Scott Anderson. (2014). Supporting disaster reconnaissance with social media data: A design-oriented case study of the 2013 Colorado floods. In and P.C. Shih. L. Plotnick M. S. P. S.R. Hiltz (Ed.), ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management (pp. 632–641). University Park, PA: The Pennsylvania State University.
Abstract: Engineering reconnaissance following an extreme event is critical in identifying the causes of infrastructure failure and minimizing such consequences in similar future events. Typically, however, much of the data about infrastructure performance and the progression of geological phenomena are lost during the event or soon after as efforts move to the recovery phase. A better methodology for reliable and rapid collection of perishable hazards data will enhance scientific inquiry and accelerate the building of disaster-resilient cities. In this paper, we explore ways to support post-event reconnaissance through the strategic collection and reuse of social media data and other remote sources of information, in response to the September 2013 flooding in Colorado. We show how tweets, particularly with postings of visual data and references to location, may be used to directly support geotechnical experts by helping to digitally survey the affected region and to navigate optimal paths through the physical space in preparation for direct observation.
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Tom De Groeve, & Patrick Riva. (2009). Early flood detection and mapping for humanitarian response. In S. J. J. Landgren (Ed.), ISCRAM 2009 – 6th International Conference on Information Systems for Crisis Response and Management: Boundary Spanning Initiatives and New Perspectives. Gothenburg: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Space-based river monitoring can provide a systematic, timely and impartial way to detect floods of humanitarian concern. This paper presents a new processing method for such data, resulting in daily flood magnitude time series for any arbitrary observation point on Earth, with lag times as short as 4h. Compared with previous work, this method uses image processing techniques and reduces the time to obtain a 6 year time series for an observation site from months to minutes, with more accurate results and global coverage. This results in a daily update of major floods in the world, with an objective measure for their magnitude, useful for early humanitarian response. Because of its full coverage, the grid-based technique also allows the automatic creation of low-resolution flood maps only hours after the satellite passes, independent of cloud coverage.
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Tom De Groeve, Zsofia Kugler, & G. Robert Brakenridge. (2007). Near real time flood alerting for the global disaster alert and coordination system. In K. Nieuwenhuis P. B. B. Van de Walle (Ed.), Intelligent Human Computer Systems for Crisis Response and Management, ISCRAM 2007 Academic Proceedings Papers (pp. 33–39). Delft: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: A new flood monitoring module is in development for the Global Disaster Alert and Coordination System (GDACS). GDACS is an information system designed to assist humanitarian responders with their decisions in the early onset after a disaster. It provides near-real time flood alerts with an initial estimate of the consequences based on computer models. Subsequently, the system gathers information in an automated way from relevant information sources such as international media, mapping and scientific organizations. The novel flood detection methodology is based on daily AMSR-E passive microwave measurement of 2500 flood prone sites on 1435 rivers in 132 countries. Alert thresholds are determined from the time series of the remote observations and these are validated using available flood archives (from 2002 to present). Preliminary results indicate a match of 47% between detected floods and flood archives. Individual tuning of thresholds per site should improve this result.
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Gonçalo De Jesus, Anabela Oliveira, Maria A. Santos, & João Palha-Fernandes. (2010). Development of a dam-break flood emergency information system. In C. Zobel B. T. S. French (Ed.), ISCRAM 2010 – 7th International Conference on Information Systems for Crisis Response and Management: Defining Crisis Management 3.0, Proceedings. Seattle, WA: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: This paper presents a new information system, SAGE-B, structured to support all fundamental data related to dams and the elements associated to an emergency in case of a dam-break flood. Data such as information about the population located in the areas at risk or the vehicles available for rescue that are located in the areas impacted by the predicted flood are always changing. In order to support an effective update of the required information for emergency management, an emergency information system was conceived and proposed. This paper describes the motivation for this research and the basic requirements from an emergency management perspective. The platform has a modular architecture, developed in open and free technologies, which allows a continuous development and improvement. Examples of future developments include a multichannel emergency warning system, flood wave real-time forecast and dam-breaching real-time monitoring models.
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Simone De Kleermaeker, Annette Zijderveld, & Bart Thonus. (2011). Training for crisis response with serious games based on early warning systems. 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 paper discusses serious games developed as part of a training program developed for a Dutch crisis response group, which acts during a (potential) flooding crisis. Training in general contributes to a wide range of learning objectives and can address different target audiences. For each combination of learning objective and target audience, the proper form of education has to be selected, ranging from self-tuition to large scale multi-party training and exercises. Serious games can be a useful and educational addition to the set of existing training tools. For operational crisis response groups a high match with real-time warning systems is essential. Our approach shows how to integrate both serious games and early warning systems for effective training and exercises. We end with our lessons learned in designing serious games based on early warning systems, in the context of a training program for a crisis response group.
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Lívia C. Degrossi, Guilherme G. Do Amaral, Eduardo S. M. De Vasconcelos, João Porto De Albuquerque, & Jo Ueyama. (2013). Using wireless sensor networks in the sensor web for flood monitoring in Brazil. In J. Geldermann and T. Müller S. Fortier F. F. T. Comes (Ed.), ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management (pp. 458–462). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: Flood is a critical problem that will increase as a result of climate changes. The problem of flooding is particularly challenging over the rainy season in tropical countries like Brazil. In this context, wireless sensor networks that are capable of sensing and reacting to water levels hold the potential of significantly reducing the damage, health-risks and financial impact of events. In this paper, we aim to outline our experiences with developing wireless sensor network for flood monitoring in Brazil. Our approach is based on Open Geospatial Consortium's (OGC) Sensor Web Enablement (SWE) standards, so as to enable the collected data to be shared in an interoperable and flexible manner. We describe the application of our approach in a real case study in the city of São Carlos/Brazil, emphasizing the challenges involved, the results achieved, and some lessons learned along the way.
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Diego Fabian Pajarito Grajales, Livia Castro Degrossi, Daniel Barros, Mohammed Rizwan Khan, Fernanda Lima E Silva, Maria Alexandra Cunha, et al. (2022). Enabling Participatory Flood Monitoring Through Cloud Services. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 213–223). Tarbes, France.
Abstract: Flooding events are more impactful due to climate change, while traditional top-down approaches to flood management give way to new initiatives that consider citizens and communities as active strategic actors. Researchers and practitioners have started to place communities in the centre of creation processes or invite them to co-design digital platforms. However, many citizen science projects re-use well-known technological components without reflecting about how the technology is able to effectively support citizen participation in data generation, including the provision of flexible data storage and exchange. This paper describes a novel digital platform design which adopts cloud services to integrate official and citizen-generated data about urban flooding. It summarises the results of a participatory design process of a digital platform to collect, store and exchange flood-related data, which includes components such as data lakes, Application Programming Interfaces (APIs), and web and mobile interfaces. This work in progress paper presents insights and lessons learned from using cloud services to enable citizen participation and engage communities with flood monitoring.
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Douglas Alem, & Alistair Clark. (2015). Insights from two-stage stochastic programming in emergency logistics. 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: This paper discusses the practical aspects and resulting insights of the results of a two-stage mathematical network flow model to help make the decisions required to get humanitarian aid quickly to needy recipients as part of a disaster relief operation. The aim of model is to plan where to best place aid inventory in preparation for possible disasters, and to make fast decisions about how best to channel aid to recipients as fast as possible. Humanitarian supply chains differ from commercial supply chains in their greater urgency of response and in the poor quality of data and increased uncertainty about important inputs such as transportation resources, aid availability, and the suddenness and degree of “demand”. The context is usually more chaotic with poor information feedback and a multiplicity of decision-makers in different aid organizations. The model attempts to handle this complexity by incorporating practical decisions, such as pre-allocation of emergency goods, transportation policy, fleet management and procurement, in an uncertainty environment featured by a scenario-based approach. Preliminary results based on the floods and landslides disaster of the Mountain Region of Rio de Janeiro state, Brazil, point to how to cope with these challenges by using the mathematical model.
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Ahmed T. Elsergany, Amy L. Griffin, Paul Tranter, & Sameer Alam. (2014). Descriptive and Geographical Analysis of Flood Disaster Evacuation Modelling. In and P.C. Shih. L. Plotnick M. S. P. S.R. Hiltz (Ed.), ISCRAM 2014 Conference Proceedings ? 11th International Conference on Information Systems for Crisis Response and Management (pp. 55–59). University Park, PA: The Pennsylvania State University.
Abstract: The planning of evacuation operations for a riverine flood disaster is vital for minimizing their negative impacts on human lives. This paper aims to develop a systematic method to model and plan evacuation trip generation and distribution for riverine floods. To achieve this aim, it adapts the transportation or Hitchcock problem, an operations research technique employed in conventional four-stage transportation modeling, and that is used to plan and model transport in normal situations, so that it is appropriate for flood disaster situations focusing on the first two stages. Concentrating on pre-flood hazard planning, our evacuation modelling considers two types of flood disaster data environments: certain environs, in which all decision variables are known, and uncertain environs, when probabilities of decision variables are considered in the evacuation plans.
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Fatehkia, M., Imran, M., & Weber, I. (2023). Towards Real-time Remote Social Sensing via Targeted Advertising. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 396–406). Omaha, USA: University of Nebraska at Omaha.
Abstract: Social media serves as an important communication channel for people affected by crises, creating a data source for emergency responders wanting to improve situational awareness. In particular, social listening on Twitter has been widely used for real-time analysis of crisis-related messages. This approach, however, is often hindered by the small fraction of (hyper-)localized content and by the inability to explicitly ask affected populations about aspects with the most operational value. Here, we explore a new form of social media data collected through targeted poll ads on Facebook. Using geo-targeted ads during flood events in six countries, we show that it is possible to collect thousands of poll responses within hours of launching the ad campaign, and at a cost of a few (US dollar) cents per response. We believe that this flexible, fast, and affordable data collection can serve as a valuable complement to existing approaches.
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Soraia Felicio, Viviane S. R. Silva, André Dargains, Paulo Roberto Azevedo Souza, Felippe Sampaio, Paulo V. R. Carvalho, et al. (2014). Stop disasters game experiment with elementary school students in Rio de Janeiro: Building safety culture. In and P.C. Shih. L. Plotnick M. S. P. S.R. Hiltz (Ed.), ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management (pp. 585–591). University Park, PA: The Pennsylvania State University.
Abstract: Currently, the city of Rio de Janeiro is is in total evidence, hosting important events such as the Pope's Francis' visit in 2013, the World Cup in 2014 and the Olympic Games in 2016. In order to make the population aware, of some environmental problems this article was produced to analyze what factors people consider dangerous. In 2011, Rio de Janeiro went through difficult times, caused by one of the biggest floods seen in the city which ended up partly destroying cities of the state's the mountain region. Kids from aged 10 to 13 years from a high school in Rio were invited to participate in a study and they had to answer questionnaires before and after playing the game. From the results obtained, we analyzed how the game “Stop Disasters” developed by the by the UN can help create awareness and learning on how to behave in flooding situations at an accelerated rate.
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Flavio Horita, Ricardo Vilela, Renata Martins, Danielle Bressiani, Gilca Palma, & João Porto de Albuquerque. (2018). Determining flooded areas using crowd sensing data and weather radar precipitation: a case study in Brazil. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 1040–1050). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Crowd sensing data (also known as crowdsourcing) are of great significance to support flood risk management. With the growing volume of available data in the past few years, researchers have used in situ sensor data to filter and prioritize volunteers' information. Nevertheless, stationary, in situ sensors are only capable of monitoring a limited region, and this could hamper proper decision-making. This study investigates the use of weather radar precipitation to support the processing of crowd sensing data with the goal of improving situation awareness in a disaster and early warnings (e.g., floods). Results from a case study carried out in the city of São Paulo, Brazil, demonstrate that weather radar data are able to validate flooded areas identified from clusters of crowd sensing data. In this manner, crowd sensing and weather radar data together can not only help engage citizens, but also generate high-quality data at finer spatial and temporal resolutions to improve the decision-making related to weather-related disaster events.
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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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Sérgio Freire, Christoph Aubrecht, & Stephanie Wegscheider. (2012). When the tsunami comes to town – Improving evacuation modeling by integrating high-resolution population exposure. In Z.Franco J. R. L. Rothkrantz (Ed.), ISCRAM 2012 Conference Proceedings – 9th International Conference on Information Systems for Crisis Response and Management. Vancouver, BC: Simon Fraser University.
Abstract: Tsunamis are a major risk for Lisbon (Portugal) coastal areas whose impacts can be extremely high, as confirmed by the past occurrence of major events. For correct risk assessment and awareness and for implementing mitigation measures, detailed simulation of exposure and evacuation is essential. This work uses a spatial modeling approach for estimating residential population distribution and exposure to tsunami flooding by individual building, and for simulating their evacuation travel time considering horizontal and vertical displacement. Results include finer evaluation of exposure to, and evacuation from, a potential tsunami, considering the specific inundation depth and building's height. This more detailed and accurate modeling of exposure to and evacuation from a potential tsunami can benefit risk assessment and contribute to more efficient Crisis Response and Management. © 2012 ISCRAM.
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Gah-Kai Leung. (2021). Reducing Flood Risks for Young People in the UK Housing Market. 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. 481–487). Blacksburg, VA (USA): Virginia Tech.
Abstract: Flooding is one of the most serious natural hazards faced in the UK. The Environment Agency estimates that in England alone, about 5.2 million properties are at risk of flooding, or roughly one in six (2009: 3). Flooding imposes significant financial, psychological and social burdens on households and these may be especially acute for young people in the property market, such as renters and first-time buyers. This paper examines how housing-related policy can help alleviate the burdens of flooding on young people in the housing market. First, it canvasses the kinds of damage inflicted when flooding affects properties. Second, it discusses the financial burdens imposed by such damage. Third, it enumerates the financial burdens and benefits of measures to protect against flooding. Fourth, it considers the non-monetary burdens of flooding, in the form of psychological and social burdens. Finally, the paper offers some policy recommendations in light of the preceding discussion.
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Gary Bennett, Lili Yang, & Boyka Simeonova. (2017). A Heuristic Approach to Flood Evacuation Planning. In eds Aurélie Montarnal Matthieu Lauras Chihab Hanachi F. B. Tina Comes (Ed.), Proceedings of the 14th International Conference on Information Systems for Crisis Response And Management (pp. 380–388). Albi, France: Iscram.
Abstract: Flood evacuation planning models are an important tool used in preparation for flooding events. Authorities use the plans generated by flood evacuation models to evacuate the population as quickly as possible. Contemporary models consider the whole solution space and use a stochastic search to explore and produce solutions. The one issue with stochastic approaches is that they cannot guarantee the optimality of the solution and it is important that the plans be of a high quality. We present a heuristically driven flood evacuation planning model; the proposed heuristic is deterministic, which allows the model to avoid this problem. The determinism of the model means that the optimality of solutions found can be readily verified.
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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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