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

目前迫切需要收集毛伊岛拉海纳大火的数据，这对正在进行的荒地和城市火灾建模工作至关重要。作为一个孤立的地方，风和环境观测有限，在这些数据源丢失之前，必须利用其他数据源来推进建模和模拟研究。从社交媒体和带时间戳的照片等多个来源获取数据，并使用人工智能增强的数据收集、处理和输入方法进行组织，将由毛伊岛的研究人员与毛伊岛的学生合作领导。这项工作将展示数据在了解社区内火灾传播以及与城市结构相互作用方面的重要性，同时还有一个额外的目标，即教育公众，使夏威夷政府和应急响应人员能够做出应对灾难的决定。这将有助于制定政策，以减少未来发生重大生命损失和财产损失的可能性。部署在夏威夷大学高性能计算（HPC）资源上的先进人工智能技术、美国国家科学基金会的先进网络基础设施协调生态系统：服务和支持（ACCESS）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必要信息。收集到的数据将被存档，并在由NSF自然灾害工程研究基础设施（NHERI）项目支持的数据仓库中公开提供。该奖项反映了美国国家科学基金会的法定使命，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。

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.