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

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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.