Canadian Forest Service Publications

Automated prediction of extreme fire weather from synoptic patterns in Northern Alberta, Canada. 2017. Lagerquist, R.; Flannigan, M.D.; Wang, X.; Marshall, G.A. Canadian Journal of Forest Research 47:1175-1183.

Year: 2017

Available from: Great Lakes Forestry Centre

Catalog ID: 38288

Language: English

CFS Availability: PDF (request by e-mail)

Available from the Journal's Web site.
DOI: 10.1139/cjfr-2017-0063

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Abstract

Wildfires burn an average of 2 million hectares per year in Canada, most of which can be attributed to only a few days of severe fire weather. These “spread days” are often associated with large-scale weather systems. We used extreme threshold values of three Canadian Fire Weather Index System (CFWIS) variables

the fine fuel moisture code (FFMC), initial spread index (ISI), and fire weather index (FWI) — as a proxy for spread days. Then we used self-organizing maps (SOMs) to predict spread days, with sea-level pressure and 500 hPa geopotential height as predictors. SOMs require many input parameters, and we performed an experiment to optimize six key parameters. For each month of the fire season (May–August), we also tested whether SOMs performed better when trained with only one month or with neighbouring months as well. Good performance (AUC of 0.8) was achieved for FFMC and ISI, while nearly good performance was achieved for FWI. To our knowledge, this is the first study to develop a machine-learning model for extreme fire weather that could be deployed in real time.

Plain Language Summary

Wildfires burn an average of 2 million hectares per year in Canada, with most of the area burned attributed to only a few days of severe fire weather. These “spread days” are often associated with large-scale weather systems. We used extreme threshold values of three Canadian Fire Weather Index System (CFWIS) variables – the Fine Fuel Moisture Code (FFMC), Initial Spread Index (ISI) and Fire Weather Index (FWI) – as a proxy for spread days. Then we used self-organizing maps (SOMs) to predict spread days, with sea-level pressure and 500-hPa geopotential height as predictors. SOMs require many input parameters, and we performed an experiment to optimize six key parameters. For each month of the fire season (May-August), we also tested whether SOMs perform better when trained with only one month or with neighbouring months as well. Good performance (AUC of 0.8) was achieved for FFMC and ISI, while nearly good performance was achieved for FWI. To our knowledge, this is the first study to develop a machine-learning model for extreme fire weather that could be deployed in real-time.

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