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

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.