LEAP-HI: Fighting Wildfires: A Data-Informed, Physics-Based Computational Framework for Probabilistic Risk Assessment and Mitigation and Emergency Response Management

在过去的20年里，美国野火造成的破坏显著增加。虽然联邦政府在扑灭野火方面的支出一直在稳步增加，但野火的严重程度也在上升。这一领先的美国繁荣、健康和基础设施工程(LEAP-HI)项目的重点是创建一个在多个空间和时间尺度上进行野火风险管理的总体计算平台。这一愿景将通过创建和整合数据分析、计算建模和基于模型的推理领域的跨学科科学技术来实现。其目标是为实时数字平台建立科学基础，该平台随着新数据的演变，动态更新区域和社区规模的长期(季节/月份)到短期(提前几周/几天)的点火前火灾风险，并近乎实时地预测火灾前线的点火后火灾行为。一旦开发完成，该计算平台将通过向决策者提供可操作的信息，为点火前风险缓解和点火后应急管理提供可操作的信息，从而提高野火管理过程的效率。关键利益相关者和公用事业公司的参与，未来劳动力的准备，以及K-12外展计划是该项目不可或缺的部分。该研究承诺为量化和预测野火风险提供严格的计算方法。预计的科学进展包括：(I)不同空间和时间尺度上的概率野火损失评估的总体计算平台，该平台随着可获得的数据而演变；(Ii)包括野火模型、城市火灾模型和社会经济模型的综合模拟框架，以预测野火的经济和社会损失；(Iii)用于城市火灾模拟的新的数据驱动的建模方法，以及用于野火引起的生活质量(QOL)变化的新的经验模型；(4)新的数据收集模块和先进的数据处理技术，以收集改进的数据，处理和注入不同来源的数据，并量化测量数据中的不确定性；(5)贝叶斯模型推理框架，通过将新的测量数据模块与野火模型相结合，量化建模不确定性，并实时更新火灾蔓延情况。从长远来看，该项目旨在为一个全面的新计算平台奠定科学基础，以预测和监测野火风险。由此产生的技术有可能对野火管理过程产生积极影响，包括开发准确的精算战略。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。

Destruction caused by wildfires in the US has significantly increased in the past two decades. While the federal government’s spending on wildfire fighting has been steadily increasing, wildfire severity has also been on the rise. The focus of this Leading Engineering for America's Prosperity, Health, and Infrastructure (LEAP-HI) project is the creation an overarching computational platform for wildfire risk management at multiple space and time scales. This vision will be accomplished by creating and integrating transdisciplinary scientific techniques in the fields of data analytics, computational modeling, and model-based inference. The objective is to develop scientific foundations for a live digital platform that evolves with new data and dynamically updates the long-term (seasons/months ahead) to short-term (weeks/days ahead) pre-ignition fire risks at regional and community scales, and predicts the post-ignition fire behavior in near-real-time at the fire front. Once developed, the computational platform will increase the efficiency of wildfire management process by providing actionable information to decision-makers for pre-ignition risk mitigation and post-ignition emergency response management. Involvement of key stakeholders and utility companies, preparation of future workforce, and K-12 outreach programs are integral parts of the project.The research promises to provide a rigorous computational approach to quantifying and predicting wildfire risk. Scientific advancements that are anticipated include: (i) an overarching computational platform for probabilistic wildfire loss assessment at different spatial and temporal scales that evolves with data as they become available; (ii) an integrated simulation framework including a wildfire model, urban-fire model, and socioeconomic model to predict the wildfire loss in terms of economic and social losses; (iii) a novel data-driven modeling approach for urban-fire simulation, and a new empirical model for change in quality-of-life (QoL) due to wildfire; (iv) new data collection modules and advanced data processing techniques to collect refined data, process and infuse different sources of data, and quantify uncertainty in the measurement data; and, (v) a Bayesian model inference framework to quantify modeling uncertainties and update the fire spread in ear-real-time by integrating new measurement data modules with the wildfire model. In perspective, the project aims to lay the scientific foundations of a holistic new computational platform to predict and monitor wildfire risk. The resulting technology has the potential to positively influence the wildfire management process, including the development of accurate actuarial strategies.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.