A genetic algorithm for forest firefighting optimization

In recent years, a large number of fires have ravaged planet Earth. A forest fire is a natural phenomenon that destroys the forest ecosystem in a given area. There are many factors that cause forest fires, for example, weather conditions, the increase of global warming and human action. Currently, there has been a growing focus on determining the ignition sources responsible for forest fires. Optimization has been widely applied in forest firefighting problems, allowing improvements in the effectiveness and speed of firefighters’ actions. The better and faster the firefighting team performs, the less damage is done. In this work, a forest firefighting resource scheduling problem is formulated in order to obtain the best ordered sequence of actions to be taken by a single firefighting resource in combating multiple ignitions. The objective is to maximize the unburned area, i.e., to minimize the burned area caused by the ignitions. A problem with 10 fire ignitions located in the district of Braga, in Portugal, was solved using a genetic algorithm. The results obtained demonstrate the usefulness and validity of this approach.This work has been supported by FCT Fundação para a Ciência e Tecnologia within the R &D Units Project Scope UIDB/00319/2020 and PCIF/GRF/0141/2019: “O3F - An Optimization Framework to reduce Forest Fire” and the PhD grant reference UI/BD/150936/2021

Application of soil water assessment tool (SWAT) to suppress wildfire at Bayam Forest, Turkey

Authors would like to thank the Central Finance and Contracts Unit (CFCU) in TURKEY and the EU INTERREG IV "Black Sea Basin Joint Operational Programme 2007-2013" framework that funded this project. In addition. we would like the staff members of the Kastamonu Regional Directorate of Forestry. Yasar Cakiroglu, Muzaffer Buyukterzi and Hidayet Kavi for their generous help and support.Aim: Readily available water resources are a key for wildfire suppression. Hydrologic models are a practical and essential tool for understanding the processes of hydrology and managing water resources, but have not been utilized as frequently for wildfire suppression. The goal of the present study was to use the Soil WaterAssessment Tools (SWAT) model to determine whether the stream water could be managed sustainably in wildfire suppression at the Bayam Forest District in Kastamonu Province, Turkey. Methodology: As an input file, the SWAT model used soils, land-uses, weather data and morphology of watershed based on the Digital Elevation Model (DEM). The model was applied for period 2001-2013 in order to predict the water budget of the study area and major streams within the studied district. Results: The analysis of the hydrologic water budget indicated that 70% (573.8 mm) of the annual precipitation (822 mm) was lost as evapotranspiration in the basin, whereas 19%, 34% and 47% of the remaining total water yield (234.6 mm) contributed to streams via surface runoff, groundwater flow and lateral flow, respectively. Interpretation: Overall, the result of SWAT model indicated to a certain degree promising findings on the availability of stream water and optimal placement of water reservoir for the use of wildfire suppression

A review of operations research methods applicable to wildfire management

Across the globe, wildfire-related destruction appears to be worsening despite increased fire suppression expenditure. At the same time, wildfire management is becoming increasingly complicated owing to factors such as an expanding wildland-urban interface, interagency resource sharing and the recognition of the beneficial effects of fire on ecosystems. Operations research is the use of analytical techniques such as mathematical modelling to analyse interactions between people, resources and the environment to aid decision-making in complex systems. Fire managers operate in a highly challenging decision environment characterised by complexity, multiple conflicting objectives and uncertainty. We assert that some of these difficulties can be resolved with the use of operations research methods. We present a range of operations research methods and discuss their applicability to wildfire management with illustrative examples drawn from the wildfire and disaster operations research literature

Iterated local search for the placement of wildland fire suppression resources

We consider the problem of, given a landscape represented by a gridded network and a fire ignition location, deciding where to locate the available fire suppression resources to minimise the burned area and the number of deployed resources as a secondary objective. We assume an estimate of the fire propagation times between adjacent nodes and use the minimum travel time principle to model the fire propagation at a landscape-level. The effect of locating a resource in a node is that it becomes protected and the fire propagation to its unburned adjacent nodes is delayed. Therefore, the problem is to identify the most promising nodes to locate the resources, which is solved by a novel iterated local search (ILS) metaheuristic. A mixed integer programming (MIP) model from the literature is used to validate the proposed method in 32 grid networks with sizes 6x6, 10x10, 20x20 and 30x30, with two different number of fire suppression resources (64 problems). Our ILS produced optimal solutions in 40 cases out of 41 known optimal lower bounds. The proposed method’s effectiveness is also due to its short computing times and small coefficients of variation of the objective function values. We also provide a categorised literature review on fire suppression deterministic optimisation models, from which we conclude that approximate collaborative approaches seldom have been applied in the past and, according to the results obtained, can successfully address the complexity of fire suppression, reaching good quality solutions even for large scale instances.This work has been supported by FCT - Fundação para a Ciência e Tecnologia within the R&D Units Project Scope UIDB/00319/2020 and within project PCIF/GRF/0141/2019 “O3F - An Optimization Framework to Reduce Forest Fire”. This paper has greatly benefit ted from the insights and suggestions of anonymous reviewers on an earlier version of the pape

Operations research for decision support in wildfire management

The February 2009 ‘Black Saturday’ bushfires resulted in 173 fatalities, caused AUD$4 billion in damage and provided a stark reminder of the destructive potential of wildfire. Globally, wildfire-related destruction appears to be worsening with observed increases in fire occurrence and severity. Wildfire management is a difficult undertaking and involves a complex mix of interrelated components operating across varying temporal and spatial scales. This thesis explores how operations research methods may be employed to provide decision support to wildfire managers so as to reduce the harmful impacts of wildfires on people, communities and natural resources. Some defining challenges of wildfire management are identified, namely complexity, multiple conflicting objectives and uncertainty. A range of operations research methods that can resolve these difficulties are then presented together with illustrative examples from the wildfire and disaster operations research literature. Three mixed integer programming models are then proposed to address specific real-world wildfire management problems. The first model incorporates the complementray effects of fuel treatment and supression preparedness decisions within an integrated framework. The second model schedules fuel treatments across multiple time periods to maintain fire resistant landscape patterns while satisfying various ecological and operational requirements. The third model aggregates fuel treatment units to minimise total perimeter requiring management

Federal Register

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