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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.