Abstract
This study explores the optimal spatial allocation of initial attack resources for firefighting in the Republic of Korea. To improve the effectiveness of Korean initial attack resources with a range of policy goals, we create a scenario optimization model that minimizes the expected number of fires not receiving a predefined response. In this study, the predefined response indicates the number of firefighting resources that must arrive at a fire before the fire escapes and becomes a large fire. We use spatially explicit GIS-based information on the ecology, fire behavior, and economic characterizations important in Korea. The data include historical fire events in the Republic of Korea from 1991 to 2007, suppression costs, and spatial information on forest fire extent. Interviews with forest managers inform the range of objective functions and policy goals we address in the decision model. Based on the geographic data, we conduct a sensitivity analysis by varying the parameters systematically. Information on the relative importance of the components of the settings helps us to identify “rules of thumb” for initial attack resource allocations in particular ecological and policy settings.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Arienti MC, Cumming SG, and 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. DOI: 10.1139/X06-188.
Cumming SG (2005) Effective fire suppression in boreal forests. Canadian Journal of Forest Research 35: 772–786. DOI: 10.1139/X04-174.
Fried JS, Gilless JK (1988) Stochastic representation of fire occurrence in a wildland fire protection planning model for California. Forest Science 34(4): 948–955.
Fried JS, Gilless JK, Spero J (2006) Analyzing initial attack on wildland fires using stochastic simulation. International Journal of Wildland Fire 15: 137–146. DOI: 10.1071/WF05027.
Haight RG, Fried JS (2007) Deploying Forest fire Suppression Resources with a Scenario-Based Standard Response Model. INFOR 45: 31–39. DOI: 10.3138/infor.45.1.31.
Hu XL, Ntaimo L (2009) Integrated simulation and optimization for wildfire containment. ACM Transactions on Modeling and Computer Simulation 19: 1–29.
Korea Forest Aviation Headquater (2011) Available online: www.fao.go.kr/eng/work0201.jsp (2011. 12. 5)
Korea Forest Service (2005) Forest Fire Control Policy and Integrated Incident Command Guidelines in Korea. Taejeon, South Korea. P 256. (KFS publishing in Korean, Taejeon)
Korea Forest Service (2010) 2009 Forest Fire Statistics. Taejeon, South Korea. Available online: http://fire.forest.go.kr/ (2010. 10. 8)
Lee B, Lee MB (2009) Spatial Patterns of Forest Fires between 1991 and 2007. Journal of Korean Institute of Fire Science and Engineering 23: 15–20. (In Korean)
Lee B, Lee Y, Lee MB, Albers HJ (2011) Stochastic Simulation Model of Fire Occurrence in the Republic of Korea. Journal of Korean Forestry Society 100: 70–78. (In Korean)
Lee Y, Lee K, Kim S (2006) Severity of Forest Fire according to Forest Stand Structure. KFRI Journal of Forest Science 69: 118–123. (In Korean)
Lee Y, Fried JS, Albers HJ, et al. (2013) Deploying initial attack resources for wildfire suppression: spatial coordination, budget constraints, and capacity constraints. Canadian Journal of Forest Research 43: 1–10. DOI:10.1139/cjfr-2011-0433.
Linderoth J, Alexander S, Stephen W (2006) The empirical behavior of sampling methods for stochastic programming. Annals of Operations Research 142: 215–241. DOI: 10.1007/s10479-006-6169-8.
MacLellan JI, Martell DL (1996) Basing airtankers for forest fire control in Ontario. Operations Research 44: 677–686. DOI: 10.1287/opre.44.5.677.
Mak WK, Morton DP, Wood RK (1999) Monte Carlo Bounding Techniques for Determining Solution Quality in Stochastic Programs. Operations Research Letters 24: 47–56. DOI: 10.1016/S0167-6377(98)00054-6.
Parks GM (1964) Development and application of a model for suppression of forest fires. Management Science 10: 760–766. DOI: 10.1287/mnsc.10.4.760.
Revelle C, Hogan K (1989) The Maximum Reliability Location Problem and α-Reliable p-Center problem: Derivatives of the Probabilistic Location Set Covering Problem. Annals of Operations Research 18: 155–174.
Serra D, Marianov V (1998) The p-median problem in a changing network: the case of Barcelona. Location Science 6: 383–394. DOI: 10.1016/S0966-8349(98)00049-7.
Snyder SA, Haight RG, ReVelle C (2004) A scenario optimization model for dynamic reserve site selection. Environmental Modeling and Assessment 9: 179–187. DOI: 10.1023/B:ENMO.0000049388.71603.7f.
Snyder LV, Daskin MS (2006) Stochastic p-robust location problems. IIE Transactions 38: 971–985. DOI: 10.1080/07408170500469113.
Turner MG, Romme WH, Tinker DB (2003) Surprises and lessons from the 1988 Yellowstone fires. Frontiers in Ecology and the Environment 1: 351–358.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Lee, Y., Lee, B. & Kim, K.H. Optimal spatial allocation of initial attack resources for firefighting in the republic of Korea using a scenario optimization model. J. Mt. Sci. 11, 323–335 (2014). https://doi.org/10.1007/s11629-013-2669-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11629-013-2669-6