RAPID: Tuning and Assessing Lahaina Wildfire Models with AI Enhanced Data

RAPID：利用 AI 增强数据调整和评估拉海纳野火模型

• 批准号: 2347372
• 负责人: David Eder
• 金额: $ 20万
• 依托单位: University of Hawaii
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2347372&HistoricalAwards=false
• 关键词: RAPID; Tuning; Assessing; Lahaina; Wildfire

There is an urgent need to collect data from the Lahaina Fire on Maui that is critical for on-going wildland and urban fire modeling efforts. Being an isolated location with limited wind and environmental observations, other data sources must be tapped to advance modeling and simulation research before these sources are lost. The data capture from multiple sources including social media and time-stamped photos, organized with AI-enhanced methods for data gathering, processing, and infusion will be led by Maui-based researchers working with Maui students. The work will show the importance of data in the understanding of fire propagation inside the community and interaction with urban structures with an additional goal of educating the public and enabling the Hawaii government and emergency response personnel to make decisions to counteract the disaster. This will aid in the development of policies to reduce the likelihood of major loss of life and property damage in the future.Advanced AI techniques deployed on High Performance Computing (HPC) resources at the University of Hawai’i, the NSF’s Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS) program and other national infrastructure will be used to process the large volumes of data to obtain required information needed to tune and validate fire propagation and atmospheric simulations. The collected data will be archived and made publicly available in the Data Depot repository supported by NSF’s Natural Hazards Engineering Research Infrastructure (NHERI) program.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.

目前迫切需要收集毛伊岛拉海纳火灾的数据，这对正在进行的荒地和城市火灾建模工作至关重要。作为一个孤立的位置，风力和环境观测有限，必须利用其他数据源来推进建模和模拟研究，以免这些数据源丢失。来自多个来源的数据捕获，包括社交媒体和时间戳照片，使用AI增强的数据收集，处理和注入方法组织，将由毛伊岛的研究人员与毛伊岛学生合作领导。这项工作将显示数据在了解社区内火灾蔓延和与城市结构互动方面的重要性，并具有教育公众和使夏威夷政府和应急响应人员能够做出应对灾难的决定的额外目标。这将有助于制定政策，以减少未来重大生命和财产损失的可能性。夏威夷大学高性能计算（HPC）资源上部署的高级人工智能技术，NSF的高级网络基础设施协调生态系统：服务&支持（ACCESS）该计划和其他国家基础设施将用于处理大量数据，以获得调整和验证火灾传播和大气模拟所需的信息。收集的数据将被存档并在NSF自然灾害工程研究基础设施（NHERI）计划支持的数据仓库中公开提供。该奖项反映了NSF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。