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Survival analysis and classification methods for forest fire size

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  • Pier-Olivier Tremblay
  • Thierry Duchesne
  • Steven G Cumming

Abstract

Factors affecting wildland-fire size distribution include weather, fuels, and fire suppression activities. We present a novel application of survival analysis to quantify the effects of these factors on a sample of sizes of lightning-caused fires from Alberta, Canada. Two events were observed for each fire: the size at initial assessment (by the first fire fighters to arrive at the scene) and the size at “being held” (a state when no further increase in size is expected). We developed a statistical classifier to try to predict cases where there will be a growth in fire size (i.e., the size at “being held” exceeds the size at initial assessment). Logistic regression was preferred over two alternative classifiers, with covariates consistent with similar past analyses. We conducted survival analysis on the group of fires exhibiting a size increase. A screening process selected three covariates: an index of fire weather at the day the fire started, the fuel type burning at initial assessment, and a factor for the type and capabilities of the method of initial attack. The Cox proportional hazards model performed better than three accelerated failure time alternatives. Both fire weather and fuel type were highly significant, with effects consistent with known fire behaviour. The effects of initial attack method were not statistically significant, but did suggest a reverse causality that could arise if fire management agencies were to dispatch resources based on a-priori assessment of fire growth potentials. We discuss how a more sophisticated analysis of larger data sets could produce unbiased estimates of fire suppression effect under such circumstances.

Suggested Citation

  • Pier-Olivier Tremblay & Thierry Duchesne & Steven G Cumming, 2018. "Survival analysis and classification methods for forest fire size," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-16, January.
  • Handle: RePEc:plo:pone00:0189860
    DOI: 10.1371/journal.pone.0189860
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    References listed on IDEAS

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    1. Jackson, Christopher, 2016. "flexsurv: A Platform for Parametric Survival Modeling in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 70(i08).
    2. David L. Martell, 2007. "Forest Fire Management," International Series in Operations Research & Management Science, in: Andres Weintraub & Carlos Romero & Trond Bjørndal & Rafael Epstein & Jaime Miranda (ed.), Handbook Of Operations Research In Natural Resources, chapter 0, pages 489-509, Springer.
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    Cited by:

    1. Dexen DZ. Xi & C.B. Dean & Stephen W. Taylor, 2020. "Modeling the duration and size of extended attack wildfires as dependent outcomes," Environmetrics, John Wiley & Sons, Ltd., vol. 31(5), August.
    2. Maombi Mbusa Masinda & Fei Li & Liu Qi & Long Sun & Tongxin Hu, 2022. "Forest fire risk estimation in a typical temperate forest in Northeastern China using the Canadian forest fire weather index: case study in autumn 2019 and 2020," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 111(1), pages 1085-1101, March.
    3. Dexen D. Z. Xi & Charmaine B. Dean & Stephen W. Taylor, 2021. "Modeling the duration and size of wildfires using joint mixture models," Environmetrics, John Wiley & Sons, Ltd., vol. 32(6), September.

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