Collaborative Research: CDI-Type II--Integrated Weather and Wildfire Simulation and Optimization for Wildfire Management

合作研究：CDI-Type II——天气与野火综合模拟及野火管理优化

• 批准号: 0941491
• 负责人: Ming Xue
• 金额: $ 48.36万
• 依托单位: University of Oklahoma Norman Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-11-01 至 2013-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0941491&HistoricalAwards=false
• 关键词: Collaborative; Research; CDI; Type; II

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).Wildfires cause great destruction including the loss of life and damage to property, infrastructure and the environment. The complexity of wildfire management arises from the uncertain dynamic interactions and dependencies among multiple system components. These include highly dynamic and nonlinear wildfire behaviors, weather conditions, and firefighting resource management. In previous research, these components have been largely treated in isolation in their own fields. To achieve effective wildfire management, decision-making support tools that integrate all these components as a whole are needed. The objective of this project is to develop new models and computation methods that integrate weather prediction, wildfire simulation, data assimilation and stochastic optimization for effective wildfire response management. In doing so, the project will make two key paradigm-shifting advances in wildfire modeling and management: 1) coupled weather and wildfire modeling and data assimilation for two-way interactive dynamic weather-wildfire prediction, and 2) Integrated wildfire simulation and stochastic optimization for wildfire containment. The project focuses on computational thinking for understanding the complexity in the natural systems of weather and wildfire behavior, and in the man-made system of firefighting resources management. Due to the stochastic and multiscale nature of the problem data associated with these systems, the project also involves data assimilation and parallel/distributed computational methods for robust weather and wildfire behavior predictions. The results of this project will aid in wildfire management to alleviate losses caused by wildfires through robust firefighting resource management decisions. The collaboration with the Texas Forest Service will enable validation and transfer of the resulting knowledge, systems and tools to real wildfire management. The results of the research will also benefit other emergency response applications such as those in homeland security. The project will provide interdisciplinary training to both undergraduate and graduate students and will develop a web-based education and training simulation environment. The project will also strengthen and complement existing K-12 outreach programs at Georgia State University (GSU), University of Oklahoma (OU), and Texas A&M University (TAMU) for minority students, and will make a concerted effort to broaden the participation of underrepresented students in research and education. Results of the research will be presented at conferences and published in refereed journals.

该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。野火造成巨大的破坏，包括生命损失和财产损失，基础设施和环境。野火管理的复杂性源于多个系统组件之间的不确定动态交互和依赖关系。这些包括高度动态和非线性的野火行为，天气条件和消防资源管理。在以前的研究中，这些组件在很大程度上被孤立地处理在自己的领域。为了实现有效的野火管理，需要将所有这些组件集成为一个整体的决策支持工具。该项目的目标是开发新的模型和计算方法，将天气预报、野火模拟、数据同化和随机优化结合起来，以实现有效的野火响应管理。在此过程中，该项目将在野火建模和管理方面取得两个关键的范式转变进展：1）耦合天气和野火建模和数据同化，用于双向交互式动态天气野火预测，以及2）集成野火模拟和野火遏制随机优化。该项目侧重于计算思维，以了解天气和野火行为的自然系统的复杂性，以及消防资源管理的人造系统。由于与这些系统相关的问题数据的随机性和多尺度性，该项目还涉及数据同化和并行/分布式计算方法，用于强大的天气和野火行为预测。该项目的成果将有助于野火管理，通过强有力的消防资源管理决策减轻野火造成的损失。与得克萨斯州林务局的合作将使验证和转让所产生的知识，系统和工具，以真实的野火管理。研究结果也将有利于其他应急响应应用，如国土安全。该项目将向本科生和研究生提供跨学科培训，并将开发一个网上教育和培训模拟环境。该项目还将加强和补充格鲁吉亚州立大学（GSU）、俄克拉荷马州大学（OSU）和德克萨斯农工大学（TAMU）现有的面向少数民族学生的K-12外展计划，并将共同努力扩大代表性不足的学生参与研究和教育。研究结果将在会议上发表，并发表在权威期刊上。