Smoothing and Bootstrapping with Application to Forest Fire Modelling

平滑和引导在森林火灾建模中的应用

• 批准号: RGPIN-2014-05593
• 负责人: Braun, Willard
• 金额: $ 1.02万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=646191
• 关键词: Smoothing; Bootstrapping; Application; Forest; Fire

Canada's economy relies on forestry, oil and natural gas making it vulnerable to forest wildfires. The 700000 hectare Richardson Backcountry fire in 2011 is indicative of the risk to communities and the economy; evacuations occurred; some extraction operations near Fort McMurray were temporaily shut down. Fire scientists have long studied the evolution of wildfires and when and how to mitigate their effects. Deterministic approaches dominate the literature, but there is a growing awareness that the modelling of uncertainty is an important component of fire management. **The main goal of my research is to add statistical rigour to the science of forest fire modelling and management. In turn, the need for improved statistical tools to solve these problems motivates the*development of statistical methodology by myself, my students and collaborators, and by the larger community of environmental statisticians.**Two fire spread models are under study: the Prometheus Wildland Fire Growth Model, a deterministic simulator developed at Alberta Environmental and Sustainable Resource Development and a lattice spread model developed by a team including myself and individuals at Ontario Ministry of Natural Resources and the Canadian Forest Service. Recently, a student and I discovered an efficient way to model the uncertainty in the burn maps produced by Prometheus. Our method needs further refinement: the existing rate of spread models which were developed from data having a multi-level structure, are to be revised using modern statistical methods. This is important, because we need good estimates, not only of the mean rate of spread, but also the variance, so that the uncertainty is correctly modelled. Because crownfire behaviour is very different from surface fire behaviour, we will develop a new statistical model to distinguish them, as well as a model to distinguish active and passive crowning. Motivated by this kind of problem, new methods are being developed for estimating curves and surfaces that satisfy given properties. These curve-fitting methods will find wide applicability in other areas of quantitaitve science.**We also plan to develop models for the forecast errors in the rate of spread and incorporate these into our randomized Prometheus simulator. The lattice spread model needs an accurate calibration method. Data from small experimental fires will be used to refine data extraction methods and parameter estimation methodology. Anisotropic filtering is useful in processing the data; this is inspiring a new class of kernel smoothers which exploit the underlying principal. Additional features of a good fire spread simulator include a model for the duration of a fire (given that wildfires are normally not detected at the time of ignition), and a model for the lofting of dangerous firebrands which start new fires at locations remote from the original fire. Longer term plans include the study of lightning and fire ignitions which uses joint modelling methods. **Fire managment problems include the modelling and monitoring of the fire weather index (FWI), an important measure of fire danger. With my students, I have begun developing spatial versions of control charts which can be used to identify FWI hotspots. To do this accurately, spatio-temporal models of FWI are required. My students and I are also studying historical trends in initial attack response time as a way of evaluating fire management effectiveness. **This statistical study of wildfire opens up ways to address larger risk management questions which are of use for long range fire management planning; the insurance industry will also benefit from such information.

加拿大的经济依赖于林业、石油和天然气，因此容易受到森林野火的影响。2011年70万公顷的理查森穷乡僻壤大火表明了社区和经济面临的风险;发生了疏散;麦克默里堡附近的一些开采作业暂时关闭。火灾科学家长期以来一直在研究野火的演变以及何时以及如何减轻其影响。 确定性的方法占主导地位的文献，但越来越多的人意识到，不确定性的建模是火灾管理的一个重要组成部分。** 我研究的主要目标是为森林火灾建模和管理科学增加统计严谨性。 反过来，对改进统计工具以解决这些问题的需求促使我自己、我的学生和合作者以及更大的环境统计学家社区开发统计方法。两个火灾蔓延模型正在研究中：普罗米修斯荒地火灾增长模型，一个确定性的模拟器在阿尔伯塔环境和可持续资源开发和格子蔓延模型开发的一个团队，包括我和个人在安大略自然资源部和加拿大森林服务。最近，我和一个学生发现了一种有效的方法来模拟普罗米修斯制作的燃烧图中的不确定性。 我们的方法需要进一步完善：现有的传播模型，从数据开发的速度具有多层次的结构，将使用现代统计方法进行修订。 这一点很重要，因为我们需要很好的估计，不仅要估计平均利差，还要估计方差，这样才能正确地对不确定性进行建模。由于树冠火行为与地面火行为非常不同，我们将开发一个新的统计模型来区分它们，以及一个模型来区分主动和被动的树冠。 受这类问题的启发，人们正在开发新的方法来估计满足给定性质的曲线和曲面。 这些曲线拟合方法将在定量科学的其他领域中找到广泛的适用性。我们还计划开发传播率预测误差的模型，并将其纳入我们的随机Prometheus模拟器。 点阵扩散模型需要一种精确的标定方法。 来自小型实验火灾的数据将用于改进数据提取方法和参数估计方法。 各向异性滤波在处理数据时很有用;这激发了一类新的利用底层原理的核平滑器。 一个好的火灾蔓延模拟器的其他功能包括一个火灾持续时间的模型（假设野火在点火时通常不会被检测到），以及一个危险的火把在远离原始火灾的地方引发新火灾的模型。 长期计划包括使用联合建模方法研究闪电和火灾。 ** 火灾管理问题包括模拟和监测火灾天气指数，这是衡量火灾危险的一个重要指标。 与我的学生，我已经开始开发空间版本的控制图，可用于确定FWI热点。 为了准确地做到这一点，时空模型的FWI是必需的。 我和我的学生也在研究最初攻击响应时间的历史趋势，作为评估火灾管理有效性的一种方式。 ** 野火的统计研究为解决更大的风险管理问题开辟了途径，这些问题可用于长期火灾管理规划;保险业也将受益于这些信息。