A model for solving the prescribed burn planning problem

Abstract

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.

Optimizing prescribed fire allocation for managing fire risk in central Catalonia

“…In the current work we developed a multi-objective optimization approach to define optimal strategies and prioritize areas for implementing prescribed fire activities as part of larger fuel management programs. Previous optimization studies explored how treatment mosaics could be optimized to most efficiently disrupt large fire spread, and mitigate risk to communities (Chung et al, 2013;Rachmawati et al, 2015;Scott et al, 2016;Wei and Yehan, 2014;Wu et al, 2013). By contrast we explored how multiple fire management objectives can be achieved specifically with prescribed fire by identifying production possibilities (Fig.…”

Detecting critical nodes in forest landscape networks to reduce wildfire spread

“…Each site was assigned a weight by ignition probability and the value under risk if a fire originating in that site is not contained by the initial response. Rachmawati et al [101] focused on rapid fuel accumulation after treatment and used site-based combinations of vegetation type and age since fire to find an optimal multi-period sequence of fuel treatments. Wei [21] applied optimization of fuel treatment at a very small scale (7×7 rows) without embedding a fire simulation model but examined the geometry of the treated areas.…”

Balancing wildlife protection and wildfire threat mitigation using a network optimization approach

“…We combine a spatial fuel treatment placement problem that minimizes the likelihood of wildfire spread to the area of concern with the problem of maintaining a corridor between isolated refuges with minimum pass-through resistance for wildlife. Our methodology leverages recent advances in optimization to assist with wildfire prevention decisions (Martell et al, 1998;Wei et al, 2008;Rachmawati et al, 2015) and target the reduction in wildfire spread and intensity (Wei et al, 2008;Minas et al, 2014;Rachmawati et al, 2015Rachmawati et al, , 2016Minas and Hearne, 2016;Alcasena et al, 2018;Gannon et al, 2019).…”