NSF Convergence Accelerator Track D: Artificial Intelligence and Community Driven Wildland Fire Innovation via a WIFIRE Commons Infrastructure for Data and Model Sharing

University Of California-San Diego, La Jolla CA

Investigators

Abstract

The NSF Convergence Accelerator supports use-inspired, team-based, multidisciplinary efforts that address challenges of national importance and will produce deliverables of value to society in the near future. The broader impact and potential societal benefit of this Convergence Accelerator Phase I project is to create the WIFIRE Commons, a data-driven, artificial intelligence (AI) enabled and model-based scientific approach that ultimately aims to limit and even prevent the devastating effects of wildfires by using advanced technologies to support fire mitigation, preparedness, response, and recovery. The combination of wildfire data, AI and the physics of fire behavior in the main design of WIFIRE Commons drives multidisciplinary collaboration and engagement with educators, municipal leaders, and fire managers to ensure the Commons is designed for translational use. Data and model sharing are core to the effort, as is strategic partnerships and close collaboration with the private and public sectors. The project team includes educators from Hispanic-serving institutions and advocates for increasing participation of women in the fire workforce and data science fields. In addition, WIFIRE Commons’ AI Gateway machine learning, scalable computing and interactive geospatial analysis tools will be applicable to any area that can benefit from modeling. This project seeks to undertake convergence research on AI integrated wildland fire research and response, and to build a framework we call the WIFIRE Commons for using AI to enable innovative optimization of the evolving combinations of physics-based wildfire models and heterogeneous data sets used to monitor and predict wildfires in real-time. The Phase I effort will contribute toward this goal through a design-thinking approach with five streams of deliverables: 1) community convergence workshops, 2) a prototype data and model commons framework, 3) use-inspired case studies to demonstrate the proposed AI innovations, 4) prototyping of educational, outreach, and public information activities; and 5) Phase II planning. The long-term vision is to create a sustainable and open source AI-driven data and model commons to facilitate and leverage collaborations to “harness AI innovations” supporting use-inspired societal and scientific wildland fire applications. Driven by design-thinking and building upon prior research by our team members (WIFIRE, MINT, QUIC-fire), the proposed WIFIRE Commons convergence research and data and model sharing framework will enable development of novel artificial intelligence techniques and reusable models that can be utilized in many applications. This Commons infrastructure will catalog, curate and integrate data and models for AI-driven fire science, maintain open programmatic access to data in a cloud-compatible form that can be integrated into the AI process through a gateway interface, and ensure provenance of data and models over time. This AI-enabled smart data/model integration will transform the agility of science based wildland fire decision making, allowing for new kinds of models and data to be assimilated rapidly and allowing an expanding base of users to understand levels of uncertainty. 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.