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: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=588498
• 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，这使得它很容易受到森林野火的影响。2011年理查森偏远地区700000公顷的大火表明了对社区和经济的风险；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和如何减轻其影响。确定性方法在文献中占据主导地位，但越来越多的人意识到不确定性的建模是火灾管理的重要组成部分。 我的研究的主要目标是为森林火灾建模和管理科学增加统计学上的严谨性。反过来，对改进统计工具以解决这些问题的需求推动了 统计方法的发展由我、我的学生和合作者以及更大的环境统计学家社区共同完成。 目前正在研究两种火灾蔓延模型：普罗米修斯荒地火灾增长模型，由艾伯塔省环境和可持续资源发展部开发的确定性模拟器，以及由安大略省自然资源部和加拿大林业局的一个团队开发的格子蔓延模型。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燃烧图中的不确定性。我们的方法需要进一步改进：现有的从具有多层次结构的数据发展而来的利差率模型将使用现代统计方法进行修订。这一点很重要，因为我们不仅需要对平均利差速度进行良好的估计，还需要对方差进行良好的估计，以便正确地模拟不确定性。由于冠火行为与地表火灾行为有很大不同，我们将开发一个新的统计模型来区分它们，以及一个区分主动和被动加冠的模型。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量化科学的其他领域找到广泛的适用性。 我们还计划开发传播速度预测误差的模型，并将这些模型合并到我们的随机普罗米修斯模拟器中。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。各向异性过滤在数据处理中很有用；这激发了一类新的核平滑器，它利用了基本原理。一个好的火灾蔓延模拟器的其他功能包括火灾持续时间的模型(鉴于野火通常在点火时没有被检测到)，以及危险煽动者的放样模型，这些煽动者在远离原始火灾的地点引发新的火灾。较长期的计划包括使用联合建模方法研究闪电和起火。 火灾管理问题包括火灾天气指数(FWI)的建模和监测，这是火灾危险的重要衡量标准。与我的学生一起，我已经开始开发空间版本的控制图，可以用来识别第一次世界大战的热点。为了准确地做到这一点，需要建立FWI的时空模型。我和我的学生也在研究初始攻击反应时间的历史趋势，以此作为评估消防管理有效性的一种方式。 这项对野火的统计研究开辟了解决更大的风险管理问题的方法，这些问题对于长期火灾管理规划是有用的；保险业也将从这些信息中受益。