Optimize landscape fuel treatment locations to create control opportunities for future fires

Weiyu,

doi:10.1139/x2012-051

Cited by 44 publications

(32 citation statements)

References 24 publications

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“…Similarly, models designed to optimize initial attack response could be updated to account for variable suppression resource needs as a function of tSDI [43]. Calculating tSDI values under different weather scenarios could be informative for gaming out how suppression opportunities change with conditions, and could further serve as the basis for prioritization of fuel treatment investments designed to enhance suppression effectiveness [44]. Analysis of tSDI values along POD boundaries could identify potential weakness in the POD network, which could also help inform prioritization of fuel treatments.…”

Section: Discussionmentioning

confidence: 99%

Analyzing Wildfire Suppression Difficulty in Relation to Protection Demand

Thompson¹,

Liu²,

Wei³

et al. 2018

Environmental Risks

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In recent years, the field of wildfire risk management has seen dramatic advances. One notable improvement is in the realm of pre-fire suppression response planning, in particular the expansion from the assessment of risks posed by fire to the assessment of opportunities to effectively manage fire. Such proactive assessment and planning is critical to ensure that suppression response strategies and tactics are more likely to be safe and efficient. In this paper we will review the state-of-the-art in wildfire suppression planning, and illustrate application of advanced planning tools on a fire-prone landscape in Colorado, USA. Specifically we will use geospatial tools to quantify a composite index of suppression difficulty, and map this layer in relation to two key protection priorities that often drive suppression response decisions: built structures, and high value watersheds. We will discuss how our assessment results can inform planning and prioritization efforts, and offer suggestions for future research.

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Section: Discussionmentioning

confidence: 99%

Analyzing Wildfire Suppression Difficulty in Relation to Protection Demand

Thompson¹,

Liu²,

Wei³

et al. 2018

Environmental Risks

Self Cite

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“…The work by Loehle [9] also comes to similar conclusions as the findings by Finney. This paper was motivated by the limited number of stochastic optimization models in the literature that incorporate crucial stochastic factors such as weather and vegetation growth to study the economic impact of fuel treatment planning. Closely related works include the work of Wei et al [10] and Wei [11] who consider the allocation of fuel treatment to minimize future expected losses using a mixed-integer programming (MIP) model. The model selects the most suitable fuel treatment at each area under consideration and takes into account of wildfire risk which includes three components; probability of burn for each area, probability of fire spreading into the next adjacent area, and conditional probability of fire spreading from an ignited area into an adjacent.…”

Section: Introductionmentioning

confidence: 99%

A Stochastic Programming Model for Fuel Treatment Management

Kabli

Ntaimo

2015

Forests

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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. OPEN ACCESSForests 2015, 6 2149

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“…( 2008 ) formulated an integer programming approach to reduce expected loss incurred on a landscape. Wei ( 2012 ) later proposed a mixed integer programming (MIP) method to locate fuel reduction treatments to set up potential control locations for future fires. Minas et al.…”

Section: Introductionmentioning

confidence: 99%

A model for solving the prescribed burn planning problem

et al. 2015

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The increasing frequency of destructive wildfires, with a consequent loss of life and property, has led to fire and land management agencies initiating extensive fuel management programs. This involves long-term planning of fuel reduction activities such as prescribed burning or mechanical clearing. In this paper, we propose a mixed integer programming (MIP) model that determines when and where fuel reduction activities should take place. The model takes into account multiple vegetation types in the landscape, their tolerance to frequency of fire events, and keeps track of the age of each vegetation class in each treatment unit. The objective is to minimise fuel load over the planning horizon. The complexity of scheduling fuel reduction activities has led to the introduction of sophisticated mathematical optimisation methods. While these approaches can provide optimum solutions, they can be computationally expensive, particularly for fuel management planning which extends across the landscape and spans long term planning horizons. This raises the question of how much better do exact modelling approaches compare to simpler heuristic approaches in their solutions. To answer this question, the proposed model is run using an exact MIP (using commercial MIP solver) and two heuristic approaches that decompose the problem into multiple single-period sub problems. The Knapsack Problem (KP), which is the first heuristic approach, solves the single period problems, using an exact MIP approach. The second heuristic approach solves the single period sub problem using a greedy heuristic approach. The three methods are compared in term of model tractability, computational time and the objective values. The model was tested using randomised data from 711 treatment units in the Barwon-Otway district of Victoria, Australia. Solutions for the exact MIP could be obtained for up to a 15-year planning only using a standard implementation of CPLEX. Both heuristic approaches can solve significantly larger problems, involving 100-year or even longer planning horizons. Furthermore there are no substantial differences in the solutions produced by the three approaches. It is concluded that for practical purposes a heuristic method is to be preferred to the exact MIP approach.

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Optimize landscape fuel treatment locations to create control opportunities for future fires

Cited by 44 publications

References 24 publications

Analyzing Wildfire Suppression Difficulty in Relation to Protection Demand

Analyzing Wildfire Suppression Difficulty in Relation to Protection Demand

A Stochastic Programming Model for Fuel Treatment Management

A model for solving the prescribed burn planning problem

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