Collaborative Research: Planning: FIRE-PLAN: Advancing Wildland Fire Analytics for Actuarial Applications and Beyond
协作研究:规划:FIRE-PLAN:推进荒地火灾分析的精算应用及其他领域
基本信息
- 批准号:2335846
- 负责人:
- 金额:$ 12.99万
- 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:2023-10-15 至 2025-09-30
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
The impacts of uncontrolled wildland fires range from the destruction of native vegetation to property damages to long-term health effects and losses of human lives. Increasing accuracy in projections of wildland fire activity, fire behavior, and wildland fire weather is the key toward developing more efficient fire control strategies and reducing the risks of wildfires. Recent studies have demonstrated that the tools of artificial intelligence (AI) can help in planning for upcoming prescribed burns by providing higher spatial and temporal fire weather forecasts and can also assist in developing more efficient strategies for wildfire risk mitigation. However, the modeling tools that are currently used to predict fire activity are largely subject to a number of temporal or spatial constraints. For instance, most deep learning (DL) approaches for wildfire risk analytics tend to be restricted in their capabilities to systematically capture the multidimensional information recorded at disparate spatio-temporal resolutions. Furthermore, such DL architectures are inherently static and do not explicitly account for complex dynamic phenomena, which is often the key behind the accurate assessment of wildfire driving factors. Finally, these models primarily rely on supervised learning approaches where a large number of task-specific labels (e.g., fire or no fire) are needed. To address these challenges in wildfire risk analytics, this project will leverage inherently interdisciplinary approaches at the interface of Earth system sciences, DL, computational topology, statistics, and actuarial sciences. The project aims to introduce the concepts of topological data analysis (TDA) to wildfire predictive modeling, coupling them with such emerging AI machinery as time-aware graph neural networks. The resulting new methods are expected to better capture the shape patterns in the wildland fire processes with respect both to time and space and to assist in a more reliable statistical assessment of wildfire risks. The new high-fidelity predictive approaches will have the potential to deliver forecasts of fire behavior, fire activity, and fire weather at multiple spatial and temporal scales under scenarios of limited, noisy, or nonexistent labeled information. To enhance the utility of the research solutions in wildfire analytics, the researchers in this project will work in close collaboration with stakeholders, particularly, focusing on the insurance sector. The project will provide multiple interdisciplinary training opportunities at the nexus of wildfire sciences, AI, and mathematical sciences at all educational levels, from undergraduate students to practicing actuaries.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.
不受控制的野火的影响范围很广,从破坏原生植被到财产损失,再到长期健康影响和人命损失。提高野火活动、火灾行为和野火天气预测的准确性是制定更有效的火灾控制策略和降低野火风险的关键。最近的研究表明,人工智能(AI)工具可以通过提供更高的空间和时间火灾天气预报来帮助规划即将到来的规定烧伤,还可以帮助制定更有效的野火风险缓解策略。然而,目前用于预测火灾活动的建模工具在很大程度上受到许多时间或空间约束。例如,大多数用于野火风险分析的深度学习(DL)方法往往局限于系统地捕获以不同时空分辨率记录的多维信息的能力。此外,这样的DL架构本质上是静态的,并没有明确说明复杂的动态现象,这通常是准确评估野火驱动因素的关键。最后,这些模型主要依赖于监督学习方法,其中大量的任务特定标签(例如,火或没有火)。为了解决野火风险分析中的这些挑战,该项目将利用地球系统科学,DL,计算拓扑学,统计学和精算科学的接口固有的跨学科方法。该项目旨在将拓扑数据分析(TDA)的概念引入野火预测建模,并将其与时间感知图神经网络等新兴人工智能机器相结合。由此产生的新方法,预计将更好地捕捉在荒地火灾过程中的时间和空间的形状模式,并协助更可靠的野火风险的统计评估。新的高保真预测方法将有可能在有限,嘈杂或不存在标记信息的情况下,在多个空间和时间尺度上提供火灾行为,火灾活动和火灾天气的预测。为了提高野火分析研究解决方案的实用性,该项目的研究人员将与利益相关者密切合作,特别是关注保险行业。该项目将为从本科生到执业精算师的所有教育水平提供野火科学、人工智能和数学科学的多个跨学科培训机会。该奖项反映了NSF的法定使命,并通过使用基金会的智力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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Yuzhou Chen其他文献
A compact, highly sensitive optical fiber temperature sensor based on a cholesteric liquid crystal polymer film
- DOI:
10.1016/j.optcom.2024.131241 - 发表时间:
2025-01-01 - 期刊:
- 影响因子:
- 作者:
Na Zhao;Xu Li;Yuzhou Chen;Dong Zhou;Yu Huang;Yongjun Liu - 通讯作者:
Yongjun Liu
Temperature-compensated optical fiber sensor for volatile organic compound gas detection based on cholesteric liquid crystal.
基于胆甾型液晶的温度补偿光纤传感器用于挥发性有机化合物气体检测。
- DOI:
10.1364/ol.427606 - 发表时间:
2021-07 - 期刊:
- 影响因子:3.6
- 作者:
Li Zeng;Zenghui Peng;Yuzhou Chen;Zhenyu Ma;Weimin Sun;Jianyang Hu;Dong Zhou;Yongjun Liu - 通讯作者:
Yongjun Liu
Room-temperature self-healing cholesteric liquid crystal elastomer with mechanochromism and tunable circularly polarized reflection
具有力致变色和可调节圆偏振反射的室温自修复胆甾型液晶弹性体
- DOI:
10.1016/j.polymer.2025.128746 - 发表时间:
2025-09-12 - 期刊:
- 影响因子:4.500
- 作者:
Yuzhou Chen;Zhigang Ran;Yaping Wang;Wenzhu Cao;Yuelan Lv;Yongjun Liu - 通讯作者:
Yongjun Liu
Dissolving microneedles: A transdermal drug delivery system for the treatment of rheumatoid arthritis
溶解微针:一种用于治疗类风湿关节炎的透皮给药系统
- DOI:
10.1016/j.ijpharm.2025.125206 - 发表时间:
2025-02-25 - 期刊:
- 影响因子:5.200
- 作者:
Xueni Wang;Jiang Yue;Shijie Guo;Aysha Rahmatulla;Shuangshuang Li;Yang Liu;Yuzhou Chen - 通讯作者:
Yuzhou Chen
Seeking and identifying time window of antibiotic treatment under emin vivo/em guidance of PbS QDs clustered microspheres based NIR-II fluorescence imaging
基于 PbS QDs 簇状微球近红外二区荧光成像在体内/体外引导下寻找和确定抗生素治疗的时间窗
- DOI:
10.1016/j.cej.2022.138584 - 发表时间:
2023-01-01 - 期刊:
- 影响因子:13.200
- 作者:
Sijia Feng;Mo Chen;Yuzhou Chen;Liman Sai;Shixian Dong;Huizhu Li;Yimeng Yang;Jian Zhang;Xing Yang;Xiaogang Xu;Yuefeng Hao;Amr Mohamed Khair Hussein Abdou;Ngoc Quyen Tran;Shiyi Chen;Yunxia Li;Jingcheng Dong;Jun Chen - 通讯作者:
Jun Chen
Yuzhou Chen的其他文献
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{{ truncateString('Yuzhou Chen', 18)}}的其他基金
Proto-OKN Theme 1: DREAM-KG: Develop Dynamic, REsponsive, Adaptive, and Multifaceted Knowledge Graphs to address homelessness with Explainable AI
Proto-OKN 主题 1:DREAM-KG:开发动态、响应式、自适应和多方面的知识图,通过可解释的人工智能解决无家可归问题
- 批准号:
2333703 - 财政年份:2023
- 资助金额:
$ 12.99万 - 项目类别:
Cooperative Agreement
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Cell Research
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Research on the Rapid Growth Mechanism of KDP Crystal
- 批准号:10774081
- 批准年份:2007
- 资助金额:45.0 万元
- 项目类别:面上项目
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