SCC-CIVIC-PG Track B: Hyperlocal Services to Prepare Rural Communities for Extreme Weather-Related Natural Disasters

美国各地的农村社区特别容易受到与极端天气相关的灾害和自然灾害的影响。随着极端天气事件的频率、严重程度和影响程度的增加，迫切需要创新装备农村社区，以提高备灾能力和复原力。然而，社区组织提供的服务往往缺乏空间粒度，学术研究产品往往忽视长期可持续性，商业产品往往不是为农村地区设计的，因为利润率很低。在这个项目中，我们将在学术研究人员、社区/民间合作伙伴和利益相关者之间发起一项以参与为导向的合作，以创建可扩展、可持续、可转移和超本地化的服务，以帮助农村社区为极端天气相关的灾害和自然灾害做好准备并从中恢复。我们的模式是专门为农村社区设计的，通过利用全国合作推广系统、推广灾害教育网络和交通技术转移中心，可以转移到美国其他地区。随着农村社区提供食物和水，这种合作的好处也流向了依赖农村地区的郊区和城市系统。此外，这个项目的主要智力贡献是物理启发的计算机视觉(CV)。这项工作将通过估计水平风速的垂直剖面，显著提高气象应用的变异系数。如果成功，这种变革性的方法将极大地提高全球天气预报的准确性，远远超出我们在本项目中关注的极端天气预报。该项目重点关注纽约州的农业部门以及支持它的当地交通/能源基础设施。主要目标是(1)测试技术可行性、财政可持续性和社会对设想的超本地化服务的接受程度，以及(2)确定和整合人力/组织资源，以创建一个农村社区应对极端天气的参与中心。我们与纽约各地的民间和社区合作伙伴合作，旨在实现三项重大创新：将传统相机转变为低成本、易于部署和高精度的气象站和垂直剖面仪；开发基于物联网的传感包以弥合关键数据差距；以及使用遗传算法和机器学习技术优化超局部极端天气预报的数值模型。然后，我们将通过创建原型超本地服务来测试整合这些创新的有效性，以改善农村社区地方公路部门的冬季风暴应急响应。利用现有的计划和联系，该团队将与合作伙伴应用各种参与性行动研究工具和机制，以促进有效的合作。该项目是对公民创新挑战计划的回应，B轨道-自然灾害的恢复力-是NSF和国土安全部的合作。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Rural communities across the US are particularly vulnerable to extreme weather-related hazards and natural disasters. With increases in frequency, severity, and levels of impact from extreme weather events,there is an imperative need to innovate equipping rural communities for greater preparedness and resilience. However, services provided by community organizations often lack spatial granularity,academic research products often ignore long-term sustainability and commercial products are often not designed for rural areas as profits margins are low. In this project, we will initiate an engagement-driven collaboration among academic researchers, community/civic partners and stakeholders to create scalable,sustainable, transferable and hyper-local services to assist rural communities with preparing for and recovering from extreme weather-related hazards and natural disasters. Designed for rural communities explicitly, our model is transferable to the rest of the US by leveraging the national Cooperative Extension system, Extension Disaster Education Network and transportation technology transfer centers. As rural communities supply food and water, the benefits from this collaboration also flow to suburban and urban systems that are dependent upon rural areas. Furthermore, the main intellectual contribution from this project is physically inspired computer vision (CV). This work will significantly advance CV for meteorological applications by estimating vertical profiles of horizontal wind speeds. If successful, this transformative approach will greatly enhance the accuracy of weather forecasts worldwide, far beyond the extreme weather forecast we focus on in this project.This project focuses on the agricultural sector in New York (NY) state and the local-level transportation/energy infrastructures that support it. The main objectives are (1) to test technological feasibility, financial sustainability and social acceptance of the envisioned hyper-local services, and (2) to identify and integrate human/organizational resources towards creating an engagement hub for rural community resiliency to extreme weather. Teaming with civic and community partners across NY, we aim at three major innovations: turning a conventional camera into a low-cost, easy-to-deploy and highly accurate weather station and vertical profiler; developing IoT-based sensing packages to bridge critical data gaps; and optimizing a numerical model for hyper-local extreme weather forecasts using a genetic algorithm and machine learning techniques. Then we will test the effectiveness of integrating these innovations by creating a prototype hyper-local service to improve winter storm emergency response for local highway departments in rural communities. Leveraging existing programs and connections, the team will apply various participatory action research tools and mechanisms with partners to promote effective collaborations. This project is in response to the Civic Innovation Challenge program, Track B—Resilience to Natural Disasters—and is a collaboration between NSF and the Department of Homeland Security.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.