Collaborative Research: RAPID: Rapid computational modeling of wildfires and management with emphasis on human activity
合作研究:RAPID:野火和管理的快速计算建模,重点关注人类活动
基本信息
- 批准号:2345256
- 负责人:
- 金额:$ 10万
- 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
- 起止时间:2023-09-15 至 2024-08-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Accurate short-term predictions of wildfire spread are essential to inform people, minimize the loss of lives and mitigate damage and cost of wildfire through effective suppression activities. It is critical to improve on these processes in the aftermath of the devastation of the Lahaina Fires. Before details are forgotten, it is critical to identify all the factors influencing wildfire spreads and to determine how to incorporate them into the rapid prediction process. This project will train Ph.D. students in computational science and modeling. This project will involve high school and community college students from the `Aina Data Stewards program on Maui. This project will develop wildfire models that have the potential to save human lives and infrastructure in future wildfires using level-set methods and Hamilton-Jacobi equations to model wildfire spread coupled to human activity during and after wildfire activity in residential zones. While level-set methods are relatively well known for wildfire modeling with coupling to data assimilation methods for real-time analysis, there is need to understand their interaction with human activity especially as it relates to evacuation and protection of property immediately after a wildfire event. A product of this research will be a new model to provide an understanding of the complex algorithmic and mathematical basis for wildfire response that can aid in resource allocation in a virtually real-time disaster situation such as the Lahaina firestorm. This project will show how to immediately deploy this model to avert the bottlenecks leading to tragedy and the required technological advances necessary to implement paradigm-shifting solutions in fire management techniques.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.
对野火蔓延的准确短期预测对于向人们提供信息、最大限度地减少生命损失以及通过有效的灭火活动减轻野火的损失和成本至关重要。在拉海纳大火造成破坏之后,必须改进这些进程。在细节被遗忘之前,确定影响野火蔓延的所有因素并确定如何将它们纳入快速预测过程至关重要。该项目将培养博士。学生在计算科学和建模。该项目将涉及毛伊岛“Aina数据管理员”方案的高中和社区大学学生。该项目将开发野火模型,这些模型有可能在未来的野火中拯救人类生命和基础设施,使用水平集方法和Hamilton-Jacobi方程来模拟在住宅区野火活动期间和之后与人类活动相结合的野火蔓延。虽然水平集方法对于耦合到数据同化方法进行实时分析的野火建模相对较为人所知,但需要了解它们与人类活动的相互作用,特别是因为它涉及野火事件后立即疏散和保护财产。这项研究的一个成果将是一个新的模型,为野火响应提供复杂的算法和数学基础的理解,这有助于在拉海纳火灾风暴等几乎实时的灾害情况下进行资源分配。该项目将展示如何立即部署该模型,以避免导致悲剧的瓶颈,以及在火灾管理技术中实施范式转变解决方案所需的技术进步。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。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Andrea Bertozzi其他文献
Incorporating Texture Features into Optical Flow for Atmospheric Wind Velocity Estimation
将纹理特征纳入光流中进行大气风速估计
- DOI:
- 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
Joel Barnett;Andrea Bertozzi;L. Vese;Igor Yanovsky - 通讯作者:
Igor Yanovsky
Encased Cantilevers and Alternative Scan Algorithms for Ultra-Gantle High Speed Atomic Force Microscopy
- DOI:
10.1016/j.bpj.2011.11.3193 - 发表时间:
2012-01-31 - 期刊:
- 影响因子:
- 作者:
Paul Ashby;Dominik Ziegler;Andreas Frank;Sindy Frank;Alex Chen;Travis Meyer;Rodrigo Farnham;Nen Huynh;Ivo Rangelow;Jen-Mei Chang;Andrea Bertozzi - 通讯作者:
Andrea Bertozzi
Andrea Bertozzi的其他文献
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{{ truncateString('Andrea Bertozzi', 18)}}的其他基金
ATD: Active Learning Activity Detection in Multiplex Networks of Geospatial-Cyber-Temporal Data
ATD:地理空间网络时空数据多重网络中的主动学习活动检测
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2318817 - 财政年份:2023
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2027438 - 财政年份:2020
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1952339 - 财政年份:2020
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$ 10万 - 项目类别:
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ATD: Algorithms for Threat Detection in Knowledge Graphs
ATD:知识图中的威胁检测算法
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$ 10万 - 项目类别:
Standard Grant
NRT-HDR: Modeling and Understanding Human Behavior: Harnessing Data from Genes to Social Networks
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1829071 - 财政年份:2018
- 资助金额:
$ 10万 - 项目类别:
Standard Grant
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1737770 - 财政年份:2017
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$ 10万 - 项目类别:
Standard Grant
Extreme-scale algorithms for geometric graphical data models in imaging, social and network science
成像、社会和网络科学中几何图形数据模型的超大规模算法
- 批准号:
1417674 - 财政年份:2014
- 资助金额:
$ 10万 - 项目类别:
Continuing Grant
Collaborative Research: Modeling, Analysis, and Control of the Spatio-temporal Dynamics of Swarm Robotic Systems
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- 批准号:
1435709 - 财政年份:2014
- 资助金额:
$ 10万 - 项目类别:
Standard Grant
Particle laden flows - theory, analysis and experiment
颗粒负载流 - 理论、分析和实验
- 批准号:
1312543 - 财政年份:2013
- 资助金额:
$ 10万 - 项目类别:
Continuing Grant
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