Collaborative Research: Fuel Treatment Planning Optimization for Wildfire Management

该奖项通过解决减少野火造成的经济损失的重要挑战，为国家的福利做出了贡献。火灾易发地区的发展和城市化进程加快，加上气候变化不断演变，大大增加了人类社区和生态系统对野火的脆弱性。尽管在灭火工作上花费了大量资金，但每年因野火造成的经济损失和生命损失仍然很高。该奖项支持通过开发具有成本效益的方法，通过燃料处理降低风险来减少野火活动的研究工作。燃料处理包括从景观中去除植被（即燃料），以减少大规模火灾的可能性和严重程度。燃料处理是野火防御的第一道防线，可能包括控制燃烧、放牧和各种类型的机械减薄的任何组合。该奖项将有助于更好地了解哪种类型的燃料处理方案和相关的决策策略更适合特定的火灾易发地区。该项目将涉及研究生和本科生，并开发课程，使各级学生接触定量方法，以解决大规模的社会问题。该合同将支持对新的顺序混合整数优化方法的研究，以确定在多个季节中适当的位置、时间和燃料处理类型，以最大限度地减少一个地区野火的预期损失。优化框架将涉及制定和解决资源约束下燃料积累和减少的非线性数学规划模型。该方法考虑了着火过程的固有不确定性，采用了鲁棒优化技术。该项目将研究描述模型重要结构特性的分析结果，并将开发专门的数值算法来解决实际大小的问题实例。该算法将在鲁棒和组合优化的交叉点利用和扩展现代技术。这些模型将使用来自德克萨斯州森林服务局的历史数据进行校准和验证，该机构负责监督德克萨斯州的森林管理。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。

This award contributes to the welfare of the nation by addressing important challenges in reducing economic losses due to wildfires. Increased development and urbanization in fire-prone areas, coupled with evolving climate changes, have significantly increased the vulnerability of human communities and ecosystems to wildfires. Even with large expenditures for fire suppression efforts, annual economic losses, as well as loss of human life, due to wildfires remain high. This award supports research efforts to reduce wildfire activity by developing cost-effective methods to reduce risk through fuel treatment. Fuel treatment involves removing vegetation (i.e., fuel) from a landscape to reduce the potential and severity of large-scale fires. Fuel treatment, which forms a first line of wildfire defense, may include any combination of controlled burning, grazing, and various types of mechanical thinning. This award will contribute to better understanding of what types of fuel treatment options and associated decision-making strategies are more appropriate for particular fire-prone regions. This project will involve both graduate and undergraduate students as well as development of courses that expose students at all levels to quantitative methods to address large-scale societal problems.This award will support research into new sequential mixed-integer optimization methods to determine the appropriate location, timing and type of fuel treatments over multiple seasons in order to minimize the expected losses from wildfires in a region. The optimization framework will involve formulating and solving non-linear mathematical programming models for fuel accumulation and reduction under resource constraints. To take into account the inherent uncertainties with respect to fire ignition, the approach employs robust optimization techniques. The project will investigate analytical results that describe important structural properties of the models and will develop specialized numerical algorithms to solve realistically-sized instances of the problem. The algorithms will leverage and extend modern techniques at the intersection of robust and combinatorial optimization. The models will be calibrated and validated using historical data from the Texas A&M Forest Service, a state agency charged with overseeing forest management in the state of Texas.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.