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Forests 2015, 6(6), 2148-2162; doi:10.3390/f6062148

A Stochastic Programming Model for Fuel Treatment Management

1
Department of Industrial and Systems Engineering, Texas A & M University, College Station, TX 77843-3131, USA
2
Department of Ecosystem Science and Management, Texas A & M University, College Station, TX 77843-2138, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Eric J. Jokela
Received: 16 March 2015 / Accepted: 29 May 2015 / Published: 15 June 2015
(This article belongs to the Special Issue Climate Change and Forest Fire)
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Abstract

This work considers a two-stage stochastic integer programming (SIP) approach for optimizing fuel treatment planning under uncertainty in weather and fire occurrence for rural forests. Given a set of areas for potentially performing fuel treatment, the problem is to decide the best treatment option for each area under uncertainty in future weather and fire occurrence. A two-stage SIP model is devised whose objective is to minimize the here-and-now cost of fuel treatment in the first-stage, plus the expected future costs due to uncertain impact from potential fires in the second-stage calculated as ecosystem services losses. The model considers four fuel treatment options: no treatment, mechanical thinning, prescribed fire, and grazing. Several constraints such as budgetary and labor constraints are included in the model and a standard fire behavior model is used to estimate some of the parameters of the model such as fuel levels at the beginning of the fire season. The SIP model was applied to data for a study area in East Texas with 15 treatment areas under different weather scenarios. The results of the study show, for example, that unless the expected ecosystem services values for an area outweigh fuel treatment costs, no treatment is the best choice for the area. Thus the valuation of the area together with the probability of fire occurrence and behavior strongly drive fuel treatment choices. View Full-Text
Keywords: fuel treatment; wildfire behavior; wildfire risk; optimization; stochastic integer programming fuel treatment; wildfire behavior; wildfire risk; optimization; stochastic integer programming
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Kabli, M.; Gan, J.; Ntaimo, L. A Stochastic Programming Model for Fuel Treatment Management. Forests 2015, 6, 2148-2162.

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