Bayes Nets as a method for analysing the influence of management actions in fire planning
T. D. Penman A B C , O. Price B and R. A. Bradstock B
+ Author Affiliations
- Author Affiliations
A Forest and Rangeland Ecosystems, Industry and Investment NSW, PO Box 100, Beecroft, NSW 2119, Australia.
B Centre for Environmental Risk Management of Bushfires, Institute of Conservation Biology and Environmental Management, University of Wollongong, Northfields, NSW 2522, Australia.
C Corresponding author. Email: tpenman@uow.edu.au
International Journal of Wildland Fire 20(8) 909-920 https://doi.org/10.1071/WF10076
Submitted: 15 July 2010 Accepted: 23 February 2011 Published: 24 October 2011
Abstract
Wildfire can result in significant economic costs with inquiries following such events often recommending an increase in management effort to reduce the risk of future losses. Currently, there are no objective frameworks in which to assess the relative merits of management actions or the synergistic way in which the various combinations may act. We examine the value of Bayes Nets as a method for assessing the risk reduction from fire management practices using a case study from a forested landscape. Specifically, we consider the relative reduction in wildfire risk from investing in prescribed burning, initial or rapid attack and suppression. The Bayes Net was developed using existing datasets, a process model and expert opinion. We compared the results of the models with the recorded fire data for an 11-year period from 1997 to 2000 with the model successfully duplicating these data. Initial attack and suppression effort had the greatest effect on the distribution of the fire sizes for a season. Bayes Nets provide a holistic model for considering the effect of multiple fire management methods on the risk of wildfires. The methods could be further advanced by including the costs of management and conducting a formal decision analysis.
Additional keywords: Bayesian Belief Network, fire suppression, initial attack, prescribed burning, risk management.
References
Akaike H (1973) Information theory as an extension of the maximum likelihood principle. In ‘Second International Symposium on Information Theory’, 2–8 September 1971, Tsahkadsor, Armenia, USSR. (Eds BN Petrov and F Csádki) pp. 267–281. (Akademiai Kiado: Budapest)Anonymous (2008) ‘Inquiry into the Impact of Public Land Management on Bushfires in Victoria.’ (Victorian Parliament, Environment and Natural Resources Committee: Melbourne)
Barton DN, Saloranta T, Moe SJ, Eggestad HO, Kuikka S (2008) Bayesian belief networks as a meta-modelling tool in integrated river basin management – pros and cons in evaluating nutrient abatement decisions under uncertainty in a Norwegian river basin. Ecological Economics 66, 91–104.
| Bayesian belief networks as a meta-modelling tool in integrated river basin management – pros and cons in evaluating nutrient abatement decisions under uncertainty in a Norwegian river basin.Crossref | GoogleScholarGoogle Scholar |
Boer MM, Sadler RJ, Wittkuhn R, McCaw L, Grierson PF (2009) Long-term impacts of prescribed burning on regional extent and incidence of wildfires – evidence from fifty years of active fire management in SW Australian forests. Forest Ecology and Management 259, 132–142.
| Long-term impacts of prescribed burning on regional extent and incidence of wildfires – evidence from fifty years of active fire management in SW Australian forests.Crossref | GoogleScholarGoogle Scholar |
Bradstock R, Davies I, Price O, Cary G (2008) Effects of climate change on bushfire threats to biodiversity, ecosystem processes and people in the Sydney region. University of Wollongong, Final Report to the New South Wales Department of Environment and Climate Change, Climate Change Impacts and Adaptation Research Project 050831. (Sydney)
Butry DT, Mercer ED, Prestemon JP, Pye JM, Holmes TP (2001) What is the price of catastrophic wildfire? Journal of Forestry 99, 9–17.
Cary GJ, Flannigan MD, Keane RE, Bradstock RA, Davies ID, Lenihan JM, Li C, Logan KA, Parsons RA (2009) Relative importance of fuel management, ignition management and weather for area burned: evidence from five landscape–fire-succession models. International Journal of Wildland Fire 18, 147–156.
| Relative importance of fuel management, ignition management and weather for area burned: evidence from five landscape–fire-succession models.Crossref | GoogleScholarGoogle Scholar |
Charniak E (1991) Bayesian Networks without tears. AI Magazine 12, 50–63.
Cohen JD (2000) Preventing disaster: home ignitability in the wildland–urban interface. Journal of Forestry 98, 15–21.
Ejsing E, Vastrup P, Madsen AL (2008) Predicting probability of default for large corporates. In ‘Bayesian Networks: a Practical Guide to Applications’. (Eds O Pourret, P Naim, BG Marcot) pp. 239–344. (Wiley: West Sussex, UK)
Ellis S, Kanowski P, Whelan R (2004) ‘National Inquiry on Bushfire Mitigation and Management.’ (Commonwealth of Australia: Canberra, ACT)
Esplin B, Gill AM, Enright NJ (2003) ‘Report of the Inquiry into the 2002–03 Victorian Bushfires.’ (Department of Premier and Cabinet: Melbourne)
Fernandes PM, Botelho HS (2003) A review of prescribed burning effectiveness in fire hazard reduction. International Journal of Wildland Fire 12, 117–128.
| A review of prescribed burning effectiveness in fire hazard reduction.Crossref | GoogleScholarGoogle Scholar |
Ganewatta G (2008) The economics of bushfire management. In ‘Community Bushfire Safety’. (Eds J Handmer, K Haynes) pp. 151–159. (CSIRO Publishing: Melbourne)
Gill AM, Christian KR, Moore PHR, Forrester RI (1987) Bushfire incidence fire hazard and fuel reduction burning. Australian Journal of Ecology 12, 299–306.
| Bushfire incidence fire hazard and fuel reduction burning.Crossref | GoogleScholarGoogle Scholar |
Granström A (1993) Spatial and temporal variation in lightning ignitions in Sweden. Journal of Vegetation Science 4, 737–744.
| Spatial and temporal variation in lightning ignitions in Sweden.Crossref | GoogleScholarGoogle Scholar |
Hirsch KG, Martell DL (1996) A review of initial attack fire crew productivity and effectiveness. International Journal of Wildland Fire 6, 199–215.
| A review of initial attack fire crew productivity and effectiveness.Crossref | GoogleScholarGoogle Scholar |
Johnson S, Mengersen K, de Waal A, Marnewick K, Cilliers D, Houser AM, Boast L (2010) Modelling cheetah relocation success in southern Africa using an iterative Bayesian Network development cycle. Ecological Modelling 221, 641–651.
| Modelling cheetah relocation success in southern Africa using an iterative Bayesian Network development cycle.Crossref | GoogleScholarGoogle Scholar |
Kaloudis S, Tocatlidou A, Lorentzos NA, Sideridis AB, Karteris M (2005) Assessing wildfire destruction danger: a decision support system incorporating uncertainty. Ecological Modelling 181, 25–38.
| Assessing wildfire destruction danger: a decision support system incorporating uncertainty.Crossref | GoogleScholarGoogle Scholar |
Keith DW (1996) When is it appropriate to combine expert judgments? Climatic Change 33, 139–143.
| When is it appropriate to combine expert judgments?Crossref | GoogleScholarGoogle Scholar |
Kenny B, Sutherland E, Tasker E, Bradstock R (2004) Guidelines for ecologically sustainable fire management. NSW National Parks and Wildlife Service. (Sydney)
King KJ, Bradstock RA, Cary GJ, Chapman J, Marsden-Smedley JB (2008) The relative importance of fine-scale fuel mosaics on reducing fire risk in south-west Tasmania, Australia. International Journal of Wildland Fire 17, 421–430.
| The relative importance of fine-scale fuel mosaics on reducing fire risk in south-west Tasmania, Australia.Crossref | GoogleScholarGoogle Scholar |
Korb KB, Nicholson AE (2004) ‘Bayesian Artificial Intelligence. Computer Science and Data Analysis.’ (CRC/Chapman Hall: Boca Raton, FL)
Krusel N, Petris SN (1992) A study of civilian deaths in the 1983 Ash Wednesday Bushfires Victoria, Australia. Country Fire Authority, CFA Occasional Paper 1. (Melbourne)
Lampin-Maillet C, Jappiot M, Long M, Bouillon C, Morge D, Ferrier J-P (2010) Mapping wildland–urban interfaces at large scales integrating housing density and vegetation aggregation for fire prevention in the south of France. Journal of Environmental Management 91, 732–741.
| Mapping wildland–urban interfaces at large scales integrating housing density and vegetation aggregation for fire prevention in the south of France.Crossref | GoogleScholarGoogle Scholar |
Larjavaara M, Kuuluvainen T, Tanskanen H, Venalainen A (2004) Variation in forest fire ignition probability in Finland. Silva Fennica 38, 253–266.
Leonard J, Blanchi R, Lipkin F, Newnham G, Siggins A, Opie K, Culvenor D, Cechet B, Corby N, Thomas C, Habili N, Jakab M, Coghlan R, Lorenzin G, Campbell D, Barwick M (2009) Building and land-use planning research after the 7th February Victorian bushfires: preliminary findings. CSIRO and Bushfires CRC, Final Report USP2008/018–CAF122-2-12. (Melbourne)
Loehle C (2004) Applying landscape principles to fire hazard reduction. Forest Ecology and Management 198, 261–267.
| Applying landscape principles to fire hazard reduction.Crossref | GoogleScholarGoogle Scholar |
Lucas P (2004) Bayesian analysis, pattern analysis, and data mining in health care. Current Opinion in Critical Care 10, 399–403.
| Bayesian analysis, pattern analysis, and data mining in health care.Crossref | GoogleScholarGoogle Scholar |
Marcot BG, Holthausen RS, Raphael MG, Rowland MM, Wisdom MJ (2001) Using Bayesian Belief Networks to evaluate fish and wildlife population viability under land management alternatives from an environmental impact statement. Forest Ecology and Management 153, 29–42.
| Using Bayesian Belief Networks to evaluate fish and wildlife population viability under land management alternatives from an environmental impact statement.Crossref | GoogleScholarGoogle Scholar |
Marcot BG, Steventon JD, Sutherland GD, McCann RK (2006) Guidelines for developing and updating Bayesian Belief Networks applied to ecological modeling and conservation. Canadian Journal of Forest Research 36, 3063–3074.
| Guidelines for developing and updating Bayesian Belief Networks applied to ecological modeling and conservation.Crossref | GoogleScholarGoogle Scholar |
McArthur AG (1966) Prescribed burning in Australian fire control. Australian Forestry 30, 4–11.
McArthur AG (1967) Fire behaviour in eucalypt forest. Australian Forestry and Timber Bureau, Leaflet Number 107. (Canberra)
McCarthy GJ, Tolhurst KG, Chatto K (2009) Overall fuel hazard guide, third edition. Department of Sustainability and Environment, Victoria, Fire Management Research Report Number 47. (Melbourne)
McCarthy MA, Cary G (2002) Fire regimes in landscapes: models and realities. In ‘Flammable Australia: the Fire Regimes and Biodiversity of a Continent’. (Eds RA Bradstock, JE Williams, AM Gill) pp. 77–93. (Cambridge University Press: Cambridge, UK)
McLeod R (2003) Inquiry into the operational response to the January 2003 Bushfires in the ACT. ACT Government, Publication number 03. (Canberra)
Morgan MG, Henrion M (1990) ‘Uncertainty: a Guide to Dealing with Uncertainty in Quantitative Risk and Policy.’ (Cambridge University Press: Cambridge, UK)
Noble IR, Bary GAV, Gill AM (1980) McArthur’s fire danger meters expressed as equations. Australian Journal of Ecology 5, 201–203.
| McArthur’s fire danger meters expressed as equations.Crossref | GoogleScholarGoogle Scholar |
NSW National Parks and Wildlife Service (2001) Wollemi National Park: plan of management. NSW National Parks and Wildlife Service. (Sydney)
NSW National Parks and Wildlife Service (2006) Fire management strategy Wollemi National Park. NSW Department of Environment and Climate Change. (Katoomba, NSW)
Nyberg JB, Marcot BG, Sulyma R (2006) Using Bayesian Belief Networks in adaptive management. Canadian Journal of Forest Research 36, 3104–3116.
| Using Bayesian Belief Networks in adaptive management.Crossref | GoogleScholarGoogle Scholar |
Oatley GC, Ewart BW (2003) Crimes analysis software: ‘pins in maps’, clustering and Bayes Net prediction. Expert Systems with Applications 25, 569–588.
| Crimes analysis software: ‘pins in maps’, clustering and Bayes Net prediction.Crossref | GoogleScholarGoogle Scholar |
Pearl J (1986) Fusion, propagation, and structuring in belief networks. Artificial Intelligence 29, 241–288.
| Fusion, propagation, and structuring in belief networks.Crossref | GoogleScholarGoogle Scholar |
Plucinski MP, Gould J, McCarthy G, Hollis J (2007) The effectiveness and efficiency of aerial firefighting in Australia: Part 1. Bushfire CRC, Technical Report A0701. (Melbourne)
Pollack HN (2003) ‘Uncertain Science... Uncertain World.’ (Cambridge University Press: Cambridge, UK)
Prestemon JP, Wear DN, Stewart FJ, Holmes TP (2006) Wildfire, timber salvage, and the economics of expediency. Forest Policy and Economics 8, 312–322.
Price OF, Bradstock RA (2011) The influence of weather and fuel management on the annual extent of unplanned fires in the Sydney region of Australia. International Journal of Wildland Fire 20, 142–151.
R Development Core Team (2007) ‘R: a Language and Environment for Statistical Computing.’ (R Foundation for Statistical Computing: Vienna, Austria)
Said A (2006) The implementation of a Bayesian Network for watershed management decisions. Water Resources Management 20, 591–605.
| The implementation of a Bayesian Network for watershed management decisions.Crossref | GoogleScholarGoogle Scholar |
Uusitalo L (2007) Advantages and challenges of Bayesian networks in environmental modelling. Ecological Modelling 203, 312–318.
| Advantages and challenges of Bayesian networks in environmental modelling.Crossref | GoogleScholarGoogle Scholar |
Valent P (1984) The Ash Wednesday bushfires in Victoria. The Medical Journal of Australia 141, 291–300.
Varis O (1995) Belief networks for modelling and assessment of environmental change. Environmetrics 6, 439–444.
| Belief networks for modelling and assessment of environmental change.Crossref | GoogleScholarGoogle Scholar |
