Data-Driven Wildland Fire Science with Applications to Fire Management Systems

野火是一个全球性的问题，除了一个大陆之外，在全球范围内都有发生。我提出的研究重点是荒地火灾，以支持加拿大的荒地火灾管理。具体来说，我正在申请国家科学研究委员会的发现基金来研究加拿大野火制度的关键特征，这些特征可以概括为三个主题。1。在大范围（省级以上）、精细尺度上增强明确的野火发生时空预测；2. 模拟野火寿命；3. 开发火灾负荷的随机时空模型，即在给定时间点活跃的野火数量，从管理的角度来看，可以在不同的空间尺度上，如区/部门、区域、省甚至国家层面上进行观察。主题1和主题2是对主题3的补充，因为他们研究了在时空中驱动火灾发生和生存的因素，这是理解火灾负荷的关键。主题1和2涉及提高对每天在景观中发生火灾的原因的理解，以及导致火灾在扑灭之前持续多久的原因（无论是自然的还是由于灭火努力）。主题1将研究机器学习技术（包括统计和基于算法的）的使用，以增强我们对火灾如何在时空中到达的理解，为火灾发生的数据驱动模型的适当评估和比较制定指导方针，重点是在如何使用这些模型来通知火灾管理操作的背景下这样做，并研究火灾季节的时间如何在加拿大各地发生变化。主题2涉及从生存分析到野火生命周期特征的方法的发展和应用，包括火灾生命周期的顺序组成部分如何相互关联。需要调查的问题包括：检测延迟或调度延迟如何影响火灾的未来生命周期？是什么驱动了灭灭事件？并且，是否存在可以修改消防管理工作的领域，以提高关键绩效指标，例如初始攻击工作的成功率。主题3将结合这些其他主题的进展来开发一个火灾负荷如何随时空变化的模型。这项工作涉及数据科学和分析工具的开发和应用，因为这项研究涉及对大型复杂时空数据集的分析。这些数据集将通过融合各种来源的数据来编制。这项研究将解决加拿大林业局最近出版的“加拿大野火科学蓝图（2019-2029）”中确定的差距。与消防管理人员的直接合作将增加向最终用户的知识传递，并为消防管理信息系统和决策支持提供实用工具。

Wildland fire is a global problem, occurring globally on all but one continent. My proposed research focuses on wildland fire to support Canadian wildland fire management. Specifically, I am requesting NSERC Discovery Grant funds to study key characteristics of Canadian wildland fire regimes that can be summarized into three themes. 1. Enhancements to large extent (provincial or larger), fine-scale spatially and temporally explicit wildland fire occurrence prediction; 2. Modelling wildland fire lifetimes; 3. Developing a stochastic space-time model for fire load, namely the number of active wildland fires at a given point in time which, from a management perspective, can be viewed on a variety of spatial scales such as at the district/sector, regional, provincial or even national level. Themes 1 and 2 feed into theme 3 as they study what drives the occurrence and survival of fires in space time, which are key to understanding fire load. Themes 1 and 2 involve developing an enhanced understanding of what drives fire occurrence on a daily basis across the landscape and what drives how long a fire survives prior to extinguishment (either naturally or due to fire suppression efforts). Theme 1 will investigate the use of machine learning techniques (both statistical and algorithm-based) to enhance our understanding of how fires arrive in space-time, develop guidelines for the appropriate assessment and comparison of data-driven models for fire occurrence with an emphasis on doing so in the context of how such models are used to inform fire management operations, and study how the timing of the fire season is changing across Canada's landscape. Theme 2 involves the development and application of methods from survival analysis to characteristics of wildland fire lifetimes, including how the sequential components of fire lifetimes may be related. Questions to be investigated include the following: How do detection delays or dispatch delays impact the future lifetime of a fire? What drives extinguishment events? And, are there areas where fire management efforts could be modified to improve key performance measures, such as the success rates of initial attack efforts. Theme 3 will combine advancements from these other themes to develop a model for how fire load varies over space-time. This work involves the development and application of data science and analytics tools since this research involves the analysis of large and complex spatio-temporal data sets. Such data sets will be compiled through the fusion of data from a variety of sources. This research will address gaps as identified in the "Blueprint for Wildland Fire Science in Canada (2019-2029)" recently published by the Canadian Forest Service. Direct collaboration with fire management staff will increase the knowledge transfer to end users and lead to practical tools for fire management information systems and decision support.