Machine learning to predict final fire size at the time of ignition
Shane R. Coffield
A D , Casey A. Graff B , Yang Chen A , Padhraic Smyth B , Efi Foufoula-Georgiou C A and James T. Randerson A
+ Author Affiliations
- Author Affiliations
A Department of Earth System Science, Croul Hall, University of California, Irvine, CA 92697, USA.
B Department of Computer Science, Donald Bren Hall, University of California, Irvine, CA 92697, USA.
C Department of Civil and Environmental Engineering, Engineering Hall 5400, University of California, Irvine, CA 92697, USA.
D Corresponding author. Email: scoffiel@uci.edu
International Journal of Wildland Fire 28(11) 861-873 https://doi.org/10.1071/WF19023
Submitted: 16 February 2019 Accepted: 15 August 2019 Published: 17 September 2019
Journal Compilation © IAWF 2019 Open Access CC BY-NC-ND
Abstract
Fires in boreal forests of Alaska are changing, threatening human health and ecosystems. Given expected increases in fire activity with climate warming, insight into the controls on fire size from the time of ignition is necessary. Such insight may be increasingly useful for fire management, especially in cases where many ignitions occur in a short time period. Here we investigated the controls and predictability of final fire size at the time of ignition. Using decision trees, we show that ignitions can be classified as leading to small, medium or large fires with 50.4 ± 5.2% accuracy. This was accomplished using two variables: vapour pressure deficit and the fraction of spruce cover near the ignition point. The model predicted that 40% of ignitions would lead to large fires, and those ultimately accounted for 75% of the total burned area. Other machine learning classification algorithms, including random forests and multi-layer perceptrons, were tested but did not outperform the simpler decision tree model. Applying the model to areas with intensive human management resulted in overprediction of large fires, as expected. This type of simple classification system could offer insight into optimal resource allocation, helping to maintain a historical fire regime and protect Alaskan ecosystems.
Additional keywords: boreal forests, decision trees, fire management, random forests, vapour pressure deficit.
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