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: 2335846
• 负责人: Yuzhou Chen
• 金额: $ 12.99万
• 依托单位: Temple University
• 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=2335846&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 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.

不受控制的荒野大火的影响范围从破坏本地植被到财产损害到长期的健康影响和人类生命的损失。野外火灾活动，火灾行为和野外火灾的预测的准确性提高是制定更有效的火灾控制策略并降低野火风险的关键。最近的研究表明，人工智能（AI）的工具可以通过提供更高的空间和时间火灾天气预测来帮助计划即将进行的规定烧伤，还可以帮助制定更有效的缓解野火风险的策略。但是，目前用于预测火灾活动的建模工具在很大程度上受到了许多时间或空间约束。例如，野火风险分析的大多数深度学习方法（DL）方法往往会受到系统地捕获在不同时空分辨率下记录的多维信息的能力。此外，这种DL架构本质上是静态的，并且不能明确说明复杂的动态现象，这通常是对野火驱动因素进行准确评估的关键。最后，这些模型主要依赖于有监督的学习方法，其中需要大量特定于任务的标签（例如火灾或不火灾）。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，统计和精算科学的界面上固有的跨学科方法。该项目旨在将拓扑数据分析（TDA）的概念介绍给野火预测性建模，并将它们与如有时间感知的图形神经网络等新兴的AI机械结合。预计所得的新方法将更好地捕获野外火灾过程中的形状模式，并协助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，嘈杂或不存在标记的信息的情况下，在多个空间和时间尺度上提供对火行为，火灾活动和火灾天气的预测。为了增强研究解决方案在野火分析中的实用性，该项目的研究人员将与利益相关者密切合作，尤其是关注保险领域。该项目将在野火科学，人工智能和数学科学的联系中提供多个跨学科的培训机会，从本科生到执业精算师。这一奖项反映了NSF的法定任务，并通过基金会的知识分子优点和广泛的影响来评估NSF的法定任务，并被认为是值得的支持。