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

• 批准号: 2335847
• 负责人: Tirtha Banerjee
• 金额: $ 7万
• 依托单位: University of California-Irvine
• 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: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2335847&HistoricalAwards=false
• 关键词: Collaborative; Research; Planning; FIRE; PLAN

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 in 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广泛，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，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战略。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限制。例如，大多数用于野火风险分析的深度学习（DL）方法在系统地捕获以不同时空分辨率记录的多维信息方面往往受到限制。此外，这种深度学习架构本质上是静态的，不能明确地解释复杂的动态现象，而这通常是准确评估野火驱动因素背后的关键。最后，这些模型主要依赖于监督学习方法，其中需要大量特定于任务的标签（例如，着火或不着火）。为了应对野火风险分析中的这些挑战，该项目将在地球系统科学、深度学习、计算拓扑、统计学和精算科学的界面上利用固有的跨学科方法。该项目旨在将拓扑数据分析（TDA）的概念引入野火预测建模，并将其与诸如时间感知图神经网络等新兴人工智能机器相结合。由此产生的新方法有望在时间和空间上更好地捕捉荒地火灾过程中的形状模式，并有助于对野火风险进行更可靠的统计评估。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，在多个时空尺度上提供火灾行为、火灾活动和火灾天气的预测。为了提高研究解决方案在野火分析中的效用，该项目的研究人员将与利益相关者密切合作，特别是专注于保险部门。该项目将在从本科生到精算师的所有教育水平上，提供野火科学、人工智能和数学科学联系的多个跨学科培训机会。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。