SBIR Phase I: CAS: DIGITAL TWIN FOR CLIMATE RESILIENCE ANALYTICS

这个小企业创新研究（SBIR）第一阶段项目通过提高公共组织，官员和应急管理人员的情景意识来增强社区对气候灾害的抵御能力。该项目的重点是利用数据革命来应对气候灾害。该团队开发了一种用于灾难准备、响应和恢复的数字孪生技术。 气候灾害（特别是飓风和洪水）是美国和世界各地社区最突出的压力源，造成可怕的身体，社会和经济困难。这项研究的成果有可能产生重大的社会效益，可以提高数百万暴露于气候危害的美国居民的公共安全，并可能通过积极的准备避免数百万美元的灾害管理成本。该项目可以改变决策者、应急管理人员和响应人员的能力，以定制他们的策略和技术，以增强应对气候灾害的态势感知。这个小型企业创新研究（SBIR）第一阶段项目深入研究了创建和设计最先进的数字孪生技术的复杂挑战，该技术利用社区规模的大数据和机器智能的力量，为社区准备，疏散措施，保护行动和紧急事件后恢复提供主动和预测性的透镜。研究活动包括：（1）创建和测试计算方法、算法和度量，用于指定近时间内在块组规模上的人口准备、疏散规划和恢复的程度;（2）利用有效的数据融合和计算工作流对基于网络的数字孪生平台的架构进行原型设计和优化，以便实施所创建的方法和算法，并以直观的方式可视化输出见解，（3）评估数字孪生技术原型中嵌入的上述计算方法在最近气候灾害事件背景下的性能;以及（4）通过现有和不断发展的合作伙伴关系，展示数字孪生原型在应急响应和管理应用中的用例。该奖项反映了NSF的法定使命，并被认为值得支持通过使用基金会的知识价值和更广泛的影响审查标准进行评估。

This Small Business Innovation Research (SBIR) Phase I project augments community resilience to climate hazards by improving the situational awareness of public organizations, officials, and emergency managers. The project is focused on harnessing the data revolution in dealing with climate hazards. The team develops a digital twin technology for disaster preparedness, response, and recovery. Climate hazards (hurricanes and floods, in particular) are the most prominent stressors for communities in the United States and worldwide, causing dire physical, social, and economic hardships. The outcomes of this research have the potential for significant societal benefits that could enhance the public safety of millions of U.S. residents exposed to climate hazards and potentially lead to millions of dollars in avoided disaster management costs through proactive preparedness. The project could transform the ability of decision-makers, emergency managers, and responders to tailor their strategies and technologies to enhance situational awareness in dealing with climate hazards. This Small Business Innovation Research (SBIR) Phase I project delves into the intricate challenges of creating and designing a state-of-the-art digital twin technology that harnesses the power of community-scale big data and machine intelligence, offering a proactive and predictive lens on community preparedness, evacuation measures, protective actions, and post-emergency event recovery. The research activities include: (1) creating and testing computational methods, algorithms and metrics for specifying the extent of a populations' preparedness, evacuation planning, and recovery at the block group scale in near-time; (2) prototyping and optimizing the architecture of a web-based digital twin platform with effective data fusion and computation workflows in order to implement the created methods and algorithms and visualize the output insights in an intuitive, timely, and decision-friendly manner; (3) evaluating the performance of the aforementioned computational methods embedded in the digital twin technology prototype in the context of recent climate hazard events; and (4) demonstrating the use case of the digital twin prototype for emergency response and management applications through existing and growing partnerships.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.