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

NSF融合加速器支持以使用为灵感、以团队为基础的多学科努力，以应对国家重要性的挑战，并将在不久的将来产生对社会有价值的成果。这一融合加速器第一阶段项目的更广泛影响和潜在社会效益是创建WIFIRE Commons，这是一种数据驱动、人工智能(AI)启用和基于模型的科学方法，最终旨在通过使用先进技术支持火灾缓解、准备、响应和恢复来限制甚至防止野火的破坏性影响。在WIFIRE Commons的主要设计中，将野火数据、人工智能和火灾行为物理结合在一起，推动了与教育工作者、市政领导人和消防管理人员的多学科合作和参与，以确保Commons设计为可翻译使用。数据和模型共享是这一努力的核心，与私营和公共部门的战略伙伴关系和密切合作也是如此。该项目团队包括来自拉美裔服务机构的教育工作者，以及倡导增加女性在消防劳动力和数据科学领域的参与。此外，WIFIRE Commons的AI Gateway机器学习、可扩展计算和交互式地理空间分析工具将适用于任何可以从建模中受益的领域。该项目旨在对集成了AI的野火研究和响应进行融合研究，并构建一个我们称为WIFIRE Commons的框架，用于使用人工智能来实现对基于物理的野火模型和用于实时监测和预测野火的异类数据集的不断演变的组合的创新优化。第一阶段的工作将通过设计思维方法为这一目标做出贡献，提供五种可交付成果：1)社区融合研讨会，2)原型数据和模型公共框架，3)受使用启发的案例研究，以演示拟议的人工智能创新，4)教育、外展和公共信息活动的原型设计，以及5)第二阶段规划。其长期愿景是创建一个可持续和开源的人工智能驱动的数据和模型Commons，以促进和利用协作，以“利用人工智能创新”来支持受使用启发的社会和科学荒野火灾应用。在设计思维的推动下，在我们团队成员(WIFIRE、MINT、Quic-Fire)先前研究的基础上，拟议的WIFIRE Commons融合研究以及数据和模型共享框架将使可用于许多应用的新型人工智能技术和可重用模型的开发成为可能。这一公共基础设施将对人工智能驱动的火灾科学的数据和模型进行编目、管理和集成，以云兼容的形式保持对数据的开放式编程访问，该形式可以通过网关接口集成到人工智能过程中，并确保数据和模型的出处。这种支持人工智能的智能数据/模型集成将改变基于科学的野地火灾决策的灵活性，允许快速吸收新型模型和数据，并允许不断扩大的用户基础了解不确定性水平。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。

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.