CDS&E/Collaborative Research: DataStorm: A Data Enabled System for End-to-End Disaster Planning and Response

自然灾害深刻地影响着我们的社会。2000至2009年间，灾害造成100万人死亡，250万人受到影响，造成约1万亿美元的损失(《2010年世界灾害报告》)。有效的灾难应对需要近乎实时的努力，使可用资源与多个战线不断变化的需求相匹配。今天的专家缺乏手段，无法向应急机构提供有效的战略，以便及时规划和应对灾害。数据驱动的备灾和应对模型和计算机模拟可在预测灾害演变和通过一套不同的干预措施有效管理紧急情况方面发挥关键作用。该项目将建立一种方法，包括(A)规划灾害应对，(B)公共信息和警告，(C)关键交通服务，(D)大规模人口护理服务，以及(E)公共卫生和医疗服务。有效地使用这种综合建模方法可能会提高安全性、生活质量和社区复原力。该项目还为博士、硕士和本科生水平的研究提供了良好的环境，学生将通过参与研究、出版以及与公共机构和数据驱动的科学和工程研究人员建立伙伴关系来接触职业道路。该项目将通过多方面的研究来增强灾害响应和社区韧性，以创建一个大数据系统，以必要的数量、速度和多样性支持数据驱动的模拟，并整合和优化灾害管理中的关键方面和决策。这包括(A)能够在统一的概率模型下协同执行多个耦合模拟的新型计算基础设施；(B)解决因需要以可扩展的方式获取、集成、建模、分析、索引和搜索来自灾害模拟和真实世界观测的大量多变量、多层、多分辨率以及相互关联和相互依赖的时空数据而产生的计算挑战；(C)新的高性能数据处理系统，以支持对具有不同资源需求和时间约束的不同领域的模拟的数值结果的连续观察。这些模型、算法和系统将被集成到灾害数据管理网络基础设施(DataStorm)中，通过与交通、公共卫生和应急管理领域的专家密切合作，实现创新应用并在灾害规划和响应方面产生广泛影响。

Natural disasters affect our society in profound ways. Between 2000 and 2009, disasters killed 1 million people, affected an additional 2.5 million individuals and caused a loss of about $1 trillion (2010 World Disasters Report). Effective disaster response requires a near-real-time effort to match available resources to shifting demands on a number of fronts. Experts today lack the means to provide emergency response agencies with validated strategies for disaster planning and response on a timely basis. Data-driven models and computer simulations for disaster preparedness and response can play a key role in predicting the evolution of disasters and effectively managing emergencies through a diverse set of intervention measures. This project will establish an approach that includes (a) planning disaster response, (b) public information and warning, (c) critical transportation services, (d) mass population care services, and (e) public health and medical services. Effective use of this integrated modeling approach may lead to enhanced safety, quality of life and community resilience. The project also provides an excellent context for doctoral, masters, and undergraduate level research and students will be introduced to career pathways through their participation in research, publication, and partnership with public agencies and data-driven science and engineering researchers.This project will enhance disaster response and community resilience through multi-faceted research to create a big data system to support data-driven simulations with the necessary volume, velocity, and variety and integrate and optimize the key aspects and decisions in disaster management. This includes (a) a novel computational infrastructure capable of executing multiple coupled simulations synergistically, under a unified probabilistic model, (b) addressing computational challenges that arise from the need to acquire, integrate, model, analyze, index, and search, in a scalable manner, large volumes of multi-variate, multi-layer, multi-resolution, and interconnected and inter-dependent spatio-temporal data that arise from disaster simulations and real-world observations, (c) a new high performance data processing system to support continuous observation of the numerical results for simulations from different domains with diverse resource demands and time constraints. These models, algorithms, and systems will be integrated into a disaster data management cyber-infrastructure (DataStorm) that will enable innovative applications and generate broad impacts--through close collaborations with domain experts from transportation, public health, and emergency management--in disaster planning and response.