课题基金 / 基金详情

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

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:
$20.0万
依托单位:
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
项目状态:
已结题
起止时间:
2023-10-01 至 2024-09-30

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中文摘要
翻译
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。作为一个与世隔绝的地点,风和环境观测有限,在这些数据源消失之前,必须利用其他数据源来推进建模和仿真研究。从包括社交媒体和带有时间戳的照片在内的多种来源捕获的数据,将由毛伊岛的研究人员领导,他们与毛伊岛的学生一起工作,用人工智能增强的方法收集、处理和输入数据。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,另一个目标是教育公众,使夏威夷政府和应急人员能够做出应对灾难的决策。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。部署在夏威夷大学高性能计算(HPC)资源上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:服务与支持(Access)计划和其他国家基础设施将被用于处理大量数据,以获得调整和验证火灾传播和大气模拟所需的必要信息。收集的数据将在NSF自然灾害工程研究基础设施(NHERI)计划支持的数据仓库中存档并公开提供。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。
英文摘要
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.
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