Journal
CANADIAN JOURNAL OF FOREST RESEARCH
Volume 47, Issue 9, Pages 1175-1183Publisher
CANADIAN SCIENCE PUBLISHING, NRC RESEARCH PRESS
DOI: 10.1139/cjfr-2017-0063
Keywords
wildland fire; fire danger; fire regimes; SOM; weather
Categories
Funding
- Alberta Agriculture and Forestry
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available