A comparative analysis of wildfire initial attack containment objectives and modelling strategies in Ontario, Canada

Kennedy Korkola; Melanie Wheatley; Jennifer Beverly; Patrick M. A. James; Mike Wotton

doi:10.1071/WF24104

RESEARCH ARTICLE (Open Access)

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A comparative analysis of wildfire initial attack containment objectives and modelling strategies in Ontario, Canada

Kennedy Korkola ^A ^# ^* , Melanie Wheatley ^A ^B ^# , Jennifer Beverly

^C , Patrick M. A. James ^A and Mike Wotton ^A ^D

+ Author Affiliations

- Author Affiliations

^A Institute of Forestry and Conservation, John H. Daniels Faculty of Architecture, Landscape and Design, University of Toronto, Toronto, ON M5S 3B3, Canada.

^B Ontario Ministry of Natural Resources, Aviation Forest Fire and Emergency Services, Sault Ste Marie, ON P6A 6V5, Canada.

^C Department of Renewable Resources, 751 General Services Building, University of Alberta, Edmonton, AB T6G 2H1, Canada.

^D Great Lakes Forestry Centre, Canadian Forest Service, Natural Resources Canada, Sault Ste Marie, ON P6A 2E5, Canada.

^* Correspondence to: kennedy.korkola@mail.utoronto.ca

^# Authors contributed equally to the paper.

International Journal of Wildland Fire 33, WF24104 https://doi.org/10.1071/WF24104

Submitted: 25 June 2024 Accepted: 19 November 2024 Published: 16 December 2024

© 2024 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of IAWF. This is an open access article distributed under the Creative Commons Attribution 4.0 International License (CC BY).

Abstract

Background

Fire management agencies use the proportion of fires classified as initial attack (IA) success as a suppression performance metric, making IA success a common indicator of suppression effectiveness in research. The criteria and definition for IA success vary based on operational objectives, making comparisons across studies difficult.

Aims

To examine the sensitivity of different time and size-based IA success definitions on model predictive accuracy and compare different modelling approaches.

Methods

Using 30 years of historical fire report data from Ontario, Canada (n = 26,171), we developed logistic regression models, bagged classification trees and random forest models to predict IA success for eight different definitions. Model predictive accuracy, sensitivity and specificity were assessed on an independent validation dataset.

Key results

The eight definitions classified between 79 and 98% of fires as IA successes. There was no clear pattern between model strength across prediction metrics and IA success definition. Logistic regression generally outperformed machine learning methods in classifying IA escapes.

Conclusions and implications

The definition of IA success does not greatly impact model performance across the modelling techniques used. Models of IA success and suppression-system performance metrics should be set with specific research or operational objectives in mind.

Keywords: containment probability, fire containment, fire management, forest fire, initial attack success, logistic regression, machine learning, suppression effectiveness, wildland fire.

References

Alberta Sustainable Resource Development (2001) Wildfire management in Alberta. Available at https://open.alberta.ca/publications/0778513785 [verified 29 August 2023]

Arienti MC, Cumming SG, Boutin S (2006) Empirical models of forest fire initial attack success probabilities: the effects of fuels, anthropogenic linear features, fire weather, and management. Canadian Journal of Forest Research 36, 3155-3166.
| Crossref | Google Scholar |

Beverly JL (2017) Time since prior wildfire affects subsequent fire containment in black spruce. International Journal of Wildland Fire 26, 919-929.
| Crossref | Google Scholar |

Bonan GB, Shugart HH (1989) Environmental factors and ecological processes in boreal forests. Annual Review of Ecology and Systematics 20(1), 1-28.
| Crossref | Google Scholar |

Brier GW (1950) Verification of forecasts expressed in terms of probability. Monthly Weather Review 78(1), 1-3.
| Crossref | Google Scholar |

Burton PJ, Parisien MA, Hicke JA, Hall RJ, Freeburn JT (2008) Large fires as agents of ecological diversity in the North American boreal forest. International Journal of Wildland Fire 17(6), 754-767.
| Crossref | Google Scholar |

Calkin DE, Gebert KM, Jones JG, Neilson RP (2005) Forest service large fire area burned and suppression expenditure trends, 1970-2002. Journal of Forestry 103, 179-183.
| Crossref | Google Scholar |

Canadian Interagency Forest Fire Centre (CIFFC) (2023) Canadian wildland fire glossary. Available at https://www.ciffc.ca/sites/default/files/2023-05/CWFM_glossary_v2023-04-24-EN.pdf [verified 3 January 2024]

Cardil A, Lorente M, Boucher D, Boucher J, Gauthier S (2019) Factors influencing fire suppression success in the province of Quebec (Canada). Canadian Journal of Forest Research 49, 531-542.
| Crossref | Google Scholar |

Collins KM, Price OF, Penman TD (2018) Suppression resource decisions are the dominant influence on containment of Australian forest and grass fires. Journal of Environmental Management 228, 373-382.
| Crossref | Google Scholar | PubMed |

De’ath G, Fabricius KE (2000) Classification and regression trees: a powerful yet simple technique for ecological data analysis. Ecology 81(11), 3178-3192.
| Crossref | Google Scholar |

Flannigan MD, Amiro BD, Logan KA, Stocks BJ, Wotton BM (2006) Forest fires and climate change in the 21st century. Mitigation and Adaptation Strategies for Global Change 11(4), 847-859.
| Crossref | Google Scholar |

Gordon (2014) Developing more common language, terminology and data standards for wildland fire management in Canada. Available at https://www.ciffc.ca/sites/default/files/2019-03/Developing_More_Common_Terminolgy_-_Final_Report.pdf [verified 23 March 2022]

Hanes CC, Wang X, Jain P, Parisien MA, Little JM, Flannigan MD (2019) Fire-regime changes in Canada over the last half century. Canadian Journal of Forest Research 49, 256-269.
| Crossref | Google Scholar |

Hosmer DW, Lemeshow S, Sturdivant RX (2013) ‘Applied logistic regression.’ 3rd edn. (John Wiley & Sons)

James G, Witten D, Hastie, Tibshirani, R (2013) ‘An introduction to statistical learning.’ 2nd edn. (Springer: New York, NY, USA)

LaCarte SML (2019) Fire containment probability in conifer fuel types in Ontario. Masters Thesis, University of Toronto, Toronto, Ontario, Canada.

Liaw A, Wiener M (2002) Classification and regression by randomForest. R News 2, 18-22.
| Google Scholar |

Marshall E, Dorph A, Holyland B, Filkov A, Penman TD (2022) Suppression resources and their influence on containment of forest fires in Victoria. International Journal of Wildland Fire 31(12), 1144-1154.
| Crossref | Google Scholar |

Martell DL (2001) Forest Fire Management. In ‘Forest Fires’. pp. 527-583. (Academic Press) 10.1016/b978-012386660-8/50017-9

Merrill DF, Alexander ME (1987) ‘Glossary of forest fire management terms.’ Canadian Committee on Forest Fire Management, National Research Council of Canada: Ottawa, ON, Canada.

National Wildfire Coordinating Group (NWCG) (2022) ‘Glossary of wildland fire terminology.’ (National Wildfire Coordinating Group) Available at https://www.nwcg.gov/glossary/a-z [verified 28 March 2022]

Nychka D, Furrer R, Paige J, Sain S (2017) fields: tools for spatial data. (R Package version 10.3). 10.5065/D6W957CT

Ontario Ministry of Natural Resources (2004) ‘Forest fire management strategy for Ontario.’ (Queen’s Printer for Ontario: Toronto, ON, Canada)

Ontario Ministry of Natural Resources and Forestry (2014) ‘Wildland fire management strategy.’ (Queen’s Printer for Ontario: Toronto, ON, Canada) Available at https://www.ontario.ca/page/wildland-fire-management-strategy [verified 21 November 2023]

Phelps N, Woolford DG (2021) Guidelines for effective evaluation and comparison of wildland fire occurrence prediction models. International Journal of Wildland Fire 30(4), 225-240.
| Crossref | Google Scholar |

Plucinski MP (2012a) Factors affecting containment area and time of Australian forest fires featuring aerial suppression. Forest Science 58, 390-398.
| Crossref | Google Scholar |

Plucinski MP (2012b) Modelling the probability of Australian grassfires escaping initial attack to aid deployment decisions. International Journal of Wildland Fire 22(4), 459-468.
| Crossref | Google Scholar |

Plucinski MP (2019) Contain and control: wildfire suppression effectiveness at incidents and across landscapes. Current Forestry Reports 5, 20-40.
| Crossref | Google Scholar |

Plucinski MP, McCarthy GJ, Hollis JJ, Gould JS (2011) The effect of aerial suppression on the containment time of Australian wildfires estimated by fire management personnel. International Journal of Wildland Fire 21(3), 219-229.
| Crossref | Google Scholar |

Plucinski MP, Dunstall S, McCarthy NF, Deutsch S, Tartaglia E, Huston C, Stephenson AG (2023) Fighting wildfires: predicting initial attack success across Victoria, Australia. International Journal of Wildland Fire 32(12), 1689-1703.
| Crossref | Google Scholar |

Podur JJ, Martell DL (2007) A simulation model of the growth and suppression of large forest fires in Ontario. International Journal of Wildland Fire 16(3), 285-294.
| Crossref | Google Scholar |

Podur J, Wotton M (2010) Will climate change overwhelm fire management capacity? Ecological Modelling 221(9), 1301-1309.
| Crossref | Google Scholar |

R Core Team (2022) ‘R: A language and environment for statistical computing.’ (R Foundation for Statistical Computing: Vienna, Austria) Available at https://www.r-project.org/ [verified 17 March 2022]

Reimer J, Thompson DK, Povak N (2019) Measuring initial attack suppression effectiveness through burn probability. Fire 2, 60.
| Crossref | Google Scholar |

Rezaei M, Lee I, Beverly J (2023) The effect of wildfire suppression resources: targeting fire groups with enhanced treatment effect. SSRN Electronic Journal. 10.2139/ssrn.4554313

Rodrigues M, Alcasena F, Vega-García C (2019) Modeling initial attack success of wildfire suppression in Catalonia, Spain. Science of The Total Environment 666, 915-927.
| Crossref | Google Scholar | PubMed |

Stocks BJ, Mason JA, Todd JB, Bosch EM, Wotton BM, Amiro BD, Flannigan MD, Hirsch KG, Logan KA, Martell DL, Skinner WR (2002) Large forest fires in Canada, 1959–1997. Journal of Geophysical Research: Atmospheres 107, FFR 5-1-FFR 5-12.
| Crossref | Google Scholar |

Tremblay PO, Duchesne T, Cumming SG (2018) Survival analysis and classification methods for forest fire size. PLoS One 13, e0189860.
| Crossref | Google Scholar | PubMed |

Van Wagner CE (1987) Development and structure of the Canadian Forest Fire Weather Index System. Forestry Technical Report. (Canadian Forest Service: Ottawa, Canada)

Wheatley M, Wotton BM, Woolford DG, Martell DL, Johnston JM (2022) Modelling initial attack success on forest fires suppressed by air attack in the province of Ontario, Canada. International Journal of Wildland Fire 31(8), 774-785.
| Crossref | Google Scholar |

Xu Y, Zhou K, Zhang F (2023) Modeling wildfire initial attack success rate based on machine learning in Liangshan, China. Forests 14(4), 740.
| Crossref | Google Scholar |

Youden WJ (1950) Index for rating diagnostic tests. Cancer 3, 32-35.
| Crossref | Google Scholar | PubMed |