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

野火是一个全球性问题，除了一个大陆外，全球都有发生。我建议的研究重点是荒地火灾，以支持加拿大荒地火灾管理。具体来说，我要求NSERC发现补助金资金，以研究加拿大荒地火灾制度的主要特点，可以概括为三个主题。1.大范围（省级以上）增强、精细尺度的空间和时间上明确的野火发生预测; 2.模拟荒地火灾寿命; 3.建立一个火灾负荷的随机时空模型，即在某一特定时间点的活跃荒地火灾数量，从管理的角度来看，可以在各种空间尺度上进行观察，例如在地区/部门、区域、省甚至国家一级。主题1和主题2是主题3的补充，因为它们研究了是什么驱动了火灾在时空中的发生和生存，这是理解火灾负荷的关键。主题1和2涉及发展的是什么驱动器火灾发生在整个景观的日常基础上，是什么驱动器火灾生存多久之前，灭火（无论是自然或由于灭火工作）的深入了解。主题1将研究机器学习技术的使用（基于统计和算法），以加强我们对火灾如何在时空中发生的理解，为火灾发生的数据驱动模型的适当评估和比较制定准则，重点是如何使用这些模型为火灾管理行动提供信息，并研究火灾季节的时间是如何在加拿大各地发生变化的。主题2涉及从生存分析到荒地火灾生命期特征的方法的发展和应用，包括火灾生命期的顺序组成部分如何可能相关。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寿命？是什么推动了自杀事件？还有，是否有可以修改消防管理工作以提高关键绩效指标的领域，例如初始攻击工作的成功率。主题3将结合联合收割机的进步，从这些其他主题开发一个模型，如何火灾负荷变化的空间时间。这项工作涉及数据科学和分析工具的开发和应用，因为这项研究涉及分析大型复杂的时空数据集。这些数据集将通过融合来自各种来源的数据汇编而成。这项研究将解决加拿大林务局最近发布的“加拿大荒地火灾科学蓝图（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转让，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.