An optimization model to prioritize fuel treatments within a landscape fuel break network

We present a mixed integer programming model for prioritizing fuel treatments within a landscape fuel break network to maximize protection against wildfires, measured by the total fire size reduction or the sum of Wildland Urban Interface areas avoided from burning. This model uses a large dataset o...

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Published in:PloS one Vol. 19; no. 12; p. e0313591
Main Authors: Nguyen, Dung, Wei, Yu, Belval, Erin J., Thompson, Matthew P., Gannon, Benjamin M., Young, Jesse D., O’Connor, Christopher D., Calkin, David E.
Format: Journal Article
Language:English
Published: United States Public Library of Science 17.12.2024
Public Library of Science (PLoS)
Subjects:
California
Computer Simulation
Conservation of Natural Resources - methods
Decisions
Design
Distribution
Ecology and Environmental Sciences
Engineering and Technology
Evaluation
Forest & brush fires
Fuels
Integer programming
Mathematical optimization
Mixed integer
Models, Theoretical
Optimization
Optimization models
Physical Sciences
Prescribed fire
Prevention
Regional planning
Simulation
Wildfires
Wildland-urban interface
California
United States
United States > US
ISSN:1932-6203, 1932-6203
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Abstract We present a mixed integer programming model for prioritizing fuel treatments within a landscape fuel break network to maximize protection against wildfires, measured by the total fire size reduction or the sum of Wildland Urban Interface areas avoided from burning. This model uses a large dataset of simulated wildfires in a large landscape to inform fuel break treatment decisions. Its mathematical formulation is concise and computationally efficient, allowing for customization and expansion to address more complex and challenging fuel break management problems in diverse landscapes. We constructed test cases for Southern California of the United States to understand model outcomes across a wide range of fire and fuel management scenarios. Results suggest optimal fuel treatment layouts within the Southern California’s fuel break network responding to various model assumptions, which offer insights for regional fuel break planning. Comparative tests between the proposed optimization model and a rule-based simulation approach indicate that the optimization model can provide significantly better solutions within reasonable solving times, highlighting its potential to support fuel break management and planning decisions.
AbstractList We present a mixed integer programming model for prioritizing fuel treatments within a landscape fuel break network to maximize protection against wildfires, measured by the total fire size reduction or the sum of Wildland Urban Interface areas avoided from burning. This model uses a large dataset of simulated wildfires in a large landscape to inform fuel break treatment decisions. Its mathematical formulation is concise and computationally efficient, allowing for customization and expansion to address more complex and challenging fuel break management problems in diverse landscapes. We constructed test cases for Southern California of the United States to understand model outcomes across a wide range of fire and fuel management scenarios. Results suggest optimal fuel treatment layouts within the Southern California’s fuel break network responding to various model assumptions, which offer insights for regional fuel break planning. Comparative tests between the proposed optimization model and a rule-based simulation approach indicate that the optimization model can provide significantly better solutions within reasonable solving times, highlighting its potential to support fuel break management and planning decisions.
We present a mixed integer programming model for prioritizing fuel treatments within a landscape fuel break network to maximize protection against wildfires, measured by the total fire size reduction or the sum of Wildland Urban Interface areas avoided from burning. This model uses a large dataset of simulated wildfires in a large landscape to inform fuel break treatment decisions. Its mathematical formulation is concise and computationally efficient, allowing for customization and expansion to address more complex and challenging fuel break management problems in diverse landscapes. We constructed test cases for Southern California of the United States to understand model outcomes across a wide range of fire and fuel management scenarios. Results suggest optimal fuel treatment layouts within the Southern California's fuel break network responding to various model assumptions, which offer insights for regional fuel break planning. Comparative tests between the proposed optimization model and a rule-based simulation approach indicate that the optimization model can provide significantly better solutions within reasonable solving times, highlighting its potential to support fuel break management and planning decisions.We present a mixed integer programming model for prioritizing fuel treatments within a landscape fuel break network to maximize protection against wildfires, measured by the total fire size reduction or the sum of Wildland Urban Interface areas avoided from burning. This model uses a large dataset of simulated wildfires in a large landscape to inform fuel break treatment decisions. Its mathematical formulation is concise and computationally efficient, allowing for customization and expansion to address more complex and challenging fuel break management problems in diverse landscapes. We constructed test cases for Southern California of the United States to understand model outcomes across a wide range of fire and fuel management scenarios. Results suggest optimal fuel treatment layouts within the Southern California's fuel break network responding to various model assumptions, which offer insights for regional fuel break planning. Comparative tests between the proposed optimization model and a rule-based simulation approach indicate that the optimization model can provide significantly better solutions within reasonable solving times, highlighting its potential to support fuel break management and planning decisions.
Audience Academic
Author Belval, Erin J.
Gannon, Benjamin M.
Wei, Yu
O’Connor, Christopher D.
Calkin, David E.
Young, Jesse D.
Thompson, Matthew P.
Nguyen, Dung
AuthorAffiliation Marshall University, UNITED STATES OF AMERICA
5 USDA Forest Service, Rocky Mountain Research Station, Missoula, Montana, United States of America
1 Department of Forest and Rangeland Stewardship, Colorado State University, Fort Collins, Colorado, United States of America
3 Pyrologix LLC, Missoula, Montana, United States of America
2 USDA Forest Service, Rocky Mountain Research Station, Fort Collins, Colorado, United States of America
4 USDA Forest Service, National Office, Fire and Aviation Management, Fort Collins, Colorado, United States of America
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/39689080$$D View this record in MEDLINE/PubMed
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  ident: pone.0313591.ref078
  publication-title: Fire behaviour as a factor in forest and rural fire suppression
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Snippet We present a mixed integer programming model for prioritizing fuel treatments within a landscape fuel break network to maximize protection against wildfires,...
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SubjectTerms California
Computer Simulation
Conservation of Natural Resources - methods
Decisions
Design
Distribution
Ecology and Environmental Sciences
Engineering and Technology
Evaluation
Forest & brush fires
Fuels
Integer programming
Mathematical optimization
Mixed integer
Models, Theoretical
Optimization
Optimization models
Physical Sciences
Prescribed fire
Prevention
Regional planning
Simulation
Wildfires
Wildland-urban interface
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Title An optimization model to prioritize fuel treatments within a landscape fuel break network
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