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态势意识，增强了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。该团队开发了一种数字孪生技术，用于备灾、救灾和灾后恢复。气候灾害（尤其是飓风和洪水）是美国和世界各地社区最突出的压力源，造成可怕的物质、社会和经济困难。这项研究的结果具有潜在的重大社会效益，可以提高数百万面临气候危害的美国居民的公共安全，并有可能通过积极的准备来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者调整战略和技术的能力，以增强应对气候灾害的态势感知。这个小企业创新研究（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了积极和预测的视角。研究活动包括：(1)创建和测试计算方法、算法和指标，用于指定街区群体在近时间尺度上的人群准备程度、疏散计划和恢复；(2)基于web的数字孪生平台的原型设计和架构优化，该平台具有有效的数据融合和计算工作流，以实现所创建的方法和算法，并以直观、及时和决策友好的方式可视化输出见解；(3)在近期气候灾害事件背景下，评估上述嵌入数字孪生技术原型的计算方法的性能；(4)通过现有和不断发展的伙伴关系，展示数字孪生原型在应急响应和管理应用中的用例。该奖项反映了美国国家科学基金会的法定使命，并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，被认为值得支持。

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.