Wildfire initial response planning using probabilistically constrained stochastic integer programming
Julián A. Gallego Arrubla A , Lewis Ntaimo A C and Curt Stripling B
+ Author Affiliations
- Author Affiliations
A Department of Industrial and Systems Engineering, Texas A&M University, 3131 TAMU, College Station, TX 77843, USA.
B Texas A&M Forest Service, 301 Tarrow Street, Suite 304, College Station, TX 77843, USA.
C Corresponding author. Email: ntaimo@tamu.edu
International Journal of Wildland Fire 23(6) 825-838 https://doi.org/10.1071/WF13204
Submitted: 5 December 2013 Accepted: 7 May 2014 Published: 28 July 2014
Abstract
This paper presents a new methodology for making strategic dozer deployment plans for wildfire initial response planning for a given fire season. This approach combines a fire behaviour simulation, a wildfire risk model and a probabilistically constrained stochastic integer programming model, and takes into account the level of risk the decision-maker is willing to take when making deployment and dispatching plans. The new methodology was applied to Texas District 12, a Texas A&M Forest Service fire planning unit located in East Texas. This study demonstrates the effect of the decision-maker’s risk attitude level on deployment decisions in terms of the dozers positioned at each operations base, fires contained and their associated wildfire risk, and total containment cost. The results show that the total number of fires contained and their associated total expected cost increase when the tolerance towards risk decreases. Thus, more dozers are deployed to operations bases in areas with high wildfire risk and a high need for initial response.
Additional keywords: dozer, initial response, probabilistic constraints, risk attitude level, standard response, stochastic integer programming, wildfire initial response planning, wildfire risk.
References
Arienti M, Cumming S, Boutin S (2006) Empirical models of forest fire initial attack success probabilities: the effects of fuels, anthopogenic linear features, fire weather, and management. Canadian Journal of Forest Research 36, 3155–3166.| Empirical models of forest fire initial attack success probabilities: the effects of fuels, anthopogenic linear features, fire weather, and management.Crossref | GoogleScholarGoogle Scholar |
Bevers M (2007) A chance constraint estimation approach to optimizing resource management under uncertainty. Canadian Journal of Forest Research 37, 2270–2280.
| A chance constraint estimation approach to optimizing resource management under uncertainty.Crossref | GoogleScholarGoogle Scholar |
Bevers M, Kent B (2007) Managing risk with chance-constrained programming. In ‘Wildfire Risk: Human Perceptions and Management Implications’. (Eds WE Martin; C Raish, B Kent) pp. 212–225. (Resources for the Future: Washington, DC)
Braun W, Jones B, Lee J, Woolford D, Wotton B (2010) Forest fire risk assessment: an illustrative example from ontario, canada. Journal of Probability and Statistics 2010, 823018
| Forest fire risk assessment: an illustrative example from ontario, canada.Crossref | GoogleScholarGoogle Scholar |
Calkin DE, Venn T, Wibbenmeyer M, Thompson MP (2013) Estimating US federal wildland fire managers’ preferences toward competing strategic suppression objectives. International Journal of Wildland Fire 22, 212–222.
| Estimating US federal wildland fire managers’ preferences toward competing strategic suppression objectives.Crossref | GoogleScholarGoogle Scholar |
Chuvieco E, Aguado I, Jurdao S, Pettinari M, Yebra M, Salas J, Hantson S, de la Riva J, Ibarra P, Rodriquez M, Echevarria M, Azqueta D, Roman M, Bastarrika A, Martinez S, Recondo C, Zapico E, Martinez-Vega F (2012) Integrating geospatial information into fire risk assessment. International Journal of Wildland Fire
| Integrating geospatial information into fire risk assessment.Crossref | GoogleScholarGoogle Scholar | [Published online early 22 October 2012]
Donovan G, Rideout D (2003a) An integer programming model to optimize resource allocation for wildfire containtment. Forest Science 49, 331–335.
Donovan G, Rideout D (2003b) A reformulation of the cost plus net value change model of wildfire economics. Forest Science 49, 318–323.
Finney M, McHugh C, Grenfell I, Riley K, Short K (2011) A simulation of probabilistic wildfire risk components for the continental united states. Stochastic Environmental Research and Risk Assessment 25, 973–1000.
| A simulation of probabilistic wildfire risk components for the continental united states.Crossref | GoogleScholarGoogle Scholar |
Fried J, Gilless J (1999) CFES2: The California Fire Economics Simulator Version 2 User’s Guide. University of California, Oakland: Division of Agriculture and Natural Resources Publication 21580.
Fried J, Gilless J, Spero J (2006) Analysing initial attack on wildland fires using stochastic simulation. International Journal of Wildland Fire 15, 137–146.
| Analysing initial attack on wildland fires using stochastic simulation.Crossref | GoogleScholarGoogle Scholar |
Gorte J, Gorte R (1979) Application of economic techniques to fire management – a status review and evaluation. USDA Forest Service, Intermountain Research Station, General Technical Report INT—GTR-53. (Ogden, UT)
Greulich F, O’Regan W (1982) Optimum use of air tankers in initial attack: selection, basing, and transfer rules. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Research Paper PSW-RP-163. (Berkeley, CA)
Haas J, Calkin D, Thompson M (2013) A national approach for integrating wildfire simulation modeling into wildland urban interface risk assessments within the united states. Landscape and Urban Planning 119, 44–53.
| A national approach for integrating wildfire simulation modeling into wildland urban interface risk assessments within the united states.Crossref | GoogleScholarGoogle Scholar |
Haight R, Fried J (2007) Deploying wildland fire suppression resources with a scenario-based standard response model. INFOR 45, 31–39.
| Deploying wildland fire suppression resources with a scenario-based standard response model.Crossref | GoogleScholarGoogle Scholar |
Hessburg PF, Reynolds KM, Keane RE, James KM, Salter RB (2010) Evaluating wildland fire danger and prioritizing vegetation and fuels treatments. In ‘Advances in Threat Assessment and their Application to Forest and Rangeland Management’. (Eds JM Pye, HM Rauscher, Y Sands, DC Lee, JS Beatty) USDA Forest Service, Pacific Northwest Research Station, General Technical Report PNW-GTR-802, pp. 329–352. (Portland, OR)
Hodgson M, Newstead R (1978) Location-allocation models for one-strike initial attack of forest by airtankers. Canadian Journal of Forest Research 8, 145–154.
| Location-allocation models for one-strike initial attack of forest by airtankers.Crossref | GoogleScholarGoogle Scholar |
Hu X, Ntaimo L (2009) Integrated simulation and optimization for wildfire containment. ACM Transactions on Modeling and Computer Simulation 19, 1–29.
| Integrated simulation and optimization for wildfire containment.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXislyls7k%3D&md5=43a39168e24a1cda9091bf3680bd6c88CAS |
IBM ILOG CPLEX (2009) IBM ILOG CPLEX Callable Library Version 12.1 C API Reference Manual. (ILOG S.A. and ILOG Inc.) Available at ftp://public.dhe.ibm.com/software/websphere/ilog/docs/optimization/cplex/refcallablelibrary.pdf [Verified 2 June 2014]
Islam K, Martell D (1998) Performance of initial attack airtanker system with interacting bases and variable initial attack ranges. Canadian Journal of Forest Research 28, 1448–1455.
| Performance of initial attack airtanker system with interacting bases and variable initial attack ranges.Crossref | GoogleScholarGoogle Scholar |
Islam K, Martell D, Posner M (2009) A time-dependent spatial queueing model for the daily deployment of airtankers for forest fire control. INFOR 47, 319–333.
| A time-dependent spatial queueing model for the daily deployment of airtankers for forest fire control.Crossref | GoogleScholarGoogle Scholar |
ISO (2013) Wildfire hazard management. (Insurance Services Office Government Solutions) Available at http://www.isogov.com/services/disaster-modeling/wildfire-management.html [Verified 2 June 2014]
Keane R, Drury S, Karaua E, Hessburgb P, Reynolds K (2010) A method for mapping fire hazard and risk across multiple scales and its application in fire management. Ecological Modelling 221, 2–18.
| A method for mapping fire hazard and risk across multiple scales and its application in fire management.Crossref | GoogleScholarGoogle Scholar |
Kourtz P (1989) Two dynamic programming algorithms for forest fire resource dispatching. Canadian Journal of Forest Research 19, 106–112.
| Two dynamic programming algorithms for forest fire resource dispatching.Crossref | GoogleScholarGoogle Scholar |
MacLellan J, Martell D (1996) Basing airtankers for forest fire control in Ontario. Operations Research 44, 677–686.
| Basing airtankers for forest fire control in Ontario.Crossref | GoogleScholarGoogle Scholar |
Mees R, Strauss D (1992) Allocating resources to large wildland fires: a model with stochastic production rates. Forest Science 38, 842–853.
Mees R, Strauss D, Chase R (1994) Minimizing the cost of wildland fire suppression: a model with uncertainty in predicted flame length and fireline width produced. Canadian Journal of Forest Research 24, 1253–1259.
| Minimizing the cost of wildland fire suppression: a model with uncertainty in predicted flame length and fireline width produced.Crossref | GoogleScholarGoogle Scholar |
Miller C, Ager A (2013) A review of recent advances in risk analysis for wildfire management. International Journal of Wildland Fire 22, 1–14.
| A review of recent advances in risk analysis for wildfire management.Crossref | GoogleScholarGoogle Scholar |
Ntaimo L, Hu X, Sun Y (2008) DEVS-FIRE: towards an integrated simulation environment for surface wildfire spread and containment. Simulation 84, 137–155.
| DEVS-FIRE: towards an integrated simulation environment for surface wildfire spread and containment.Crossref | GoogleScholarGoogle Scholar |
Ntaimo L, Gallego-Arrubla J, Stripling C, Young J, Spencer T (2012) A stochastic programming standard response model for wildfire initial attack planning. Canadian Journal of Forest Research 42, 987–1001.
| A stochastic programming standard response model for wildfire initial attack planning.Crossref | GoogleScholarGoogle Scholar |
Ntaimo L, Gallego-Arrubla J, Stripling C, Young J, Spencer T (2013) A simulation and stochastic integer programming approach to wildfire initial attack planning. Forest Science 59, 105–117.
| A simulation and stochastic integer programming approach to wildfire initial attack planning.Crossref | GoogleScholarGoogle Scholar |
NWCG (2013) Wildland fire incident management field guide. (National Wildfire Coordinating Group) Available at http://www.nwcg.gov/pms/pubs/pms210/pms210.pdf [Verified 2 June 2014]
Parks G (1964) Development and application of a model for suppression of forest fires. Management Science 10, 760–766.
| Development and application of a model for suppression of forest fires.Crossref | GoogleScholarGoogle Scholar |
Plucinski M (2013) Modelling the probability of Australian grassfires escaping initial attack to aid deployment decisions. International Journal of Wildland Fire 22, 459–468.
| Modelling the probability of Australian grassfires escaping initial attack to aid deployment decisions.Crossref | GoogleScholarGoogle Scholar |
Rideout DB, Ziesler PS, Kling R, Loomis JG, Botti SJ (2008) Estimating rates of substitution for protecting values at risk for initial-attack planning and budgeting. Forest Policy and Economics 10, 205–219.
| Estimating rates of substitution for protecting values at risk for initial-attack planning and budgeting.Crossref | GoogleScholarGoogle Scholar |
Ruszczynski A, Shapiro A (2003) ‘Handbooks in Operations Research and Management Science, 10: Stochastic Programming’. (Elsevier: Boston, MA)
Thompson M, Calkin D, Finney M, Ager A, Gilbertson-Day J (2011) Integrated national-scale assessment of wildfire risk to human and ecological values. Stochastic Environmental Research and Risk Assessment 25, 761–780.
| Integrated national-scale assessment of wildfire risk to human and ecological values.Crossref | GoogleScholarGoogle Scholar |
Tolhurst K, Shields B, Chong D (2008) Phoenix: development and application of a bushfire risk management tool. The Australian Journal of Emergency Management 23, 47–54.
Tutsch M, Haider W, Beardmore B, Lertzman K, Cooper AB, Walker RC (2010) Estimating the consequences of wildfire for wildfire risk assessment, a case study in the southern gulf islands, British Columbia, Canada. Canadian Journal of Forest Research 40, 2104–2114.
| Estimating the consequences of wildfire for wildfire risk assessment, a case study in the southern gulf islands, British Columbia, Canada.Crossref | GoogleScholarGoogle Scholar |
Wiitala MR (1999) A dynamic programming approach to determining optimal forest wildfire initial attack responses. In ‘Symposium on Fire Economics, Planning, and Policy: Bottom Lines’, 5–9 April 1999, San Diego, CA. (Eds A González-Cabán, PN Omi) USDA Forest Service, Pacific Southwest Research Station, General Technical Report PSW-GTR-173, pp. 115–123. (Albany, CA)
Wiitala MR, Wilson AE (2008) Wildfire initial response assessment system: a tool for fire preparedness planning. In ‘Proceedings of the Second International Symposium on Fire Economics, Planning, and Policy: a Global View’, 19–22 April 2004, Córdoba, Spain. (Ed. A González-Cabán) USDA Forest Service, Pacific Southwest Research Station, General Technical Report PSW-GTR-208, pp. 703–707. (Albany, CA)
