IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v91y2018i1d10.1007_s11069-017-3127-5.html
   My bibliography  Save this article

Forest fire spread simulation algorithm based on cellular automata

Author

Listed:
  • Xiaoping Rui

    (University of Chinese Academy of Sciences)

  • Shan Hui

    (Land Surveying, Planning and Design Institute of Shaanxi Provincial Land Engineering Construction Group)

  • Xuetao Yu

    (Shijiazhuang Tiedao University)

  • Guangyuan Zhang

    (University of Chinese Academy of Sciences)

  • Bin Wu

    (University of Chinese Academy of Sciences)

Abstract

Traditional models result in low efficiency and poor accuracy when simulating the spread of large-scale forest fires. We constructed an improved model that couples cellular automata with an existing forest fire model to ensure better time accuracy of forest fire spread. Our model considers the impact of time steps on simulation accuracy to provide an optimal time step value. The model was tested using a case study of forest fire spread at Daxing’an Mountain in May 2006. The results show that the optimal time step for the forest fire spread geographic cellular automata simulation algorithm is 1/8 of the time taken for cellular material to be completely combusted. When compared with real fire data from Landsat Thematic Mapper (TM) images, our model was found to have high temporal and spatial consistency, with a mean Kappa coefficient of 0.6352 and mean accuracy of 87.89%. This algorithm can be used to simulate and predict forest fire spread and is also reversible (i.e., it can identify fire source points).

Suggested Citation

  • Xiaoping Rui & Shan Hui & Xuetao Yu & Guangyuan Zhang & Bin Wu, 2018. "Forest fire spread simulation algorithm based on cellular automata," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 91(1), pages 309-319, March.
    • Handle: RePEc:spr:nathaz:v:91:y:2018:i:1:d:10.1007_s11069-017-3127-5
    DOI: 10.1007/s11069-017-3127-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-017-3127-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11069-017-3127-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Duff, Thomas J. & Chong, Derek M. & Tolhurst, Kevin G., 2015. "Using discrete event simulation cellular automata models to determine multi-mode travel times and routes of terrestrial suppression resources to wildland fires," European Journal of Operational Research, Elsevier, vol. 241(3), pages 763-770.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chen Cheng & Hui Zhou & Xuchao Chai & Yang Li & Danning Wang & Yao Ji & Shichuan Niu & Ying Hou, 2020. "Adoption of image surface parameters under moving edge computing in the construction of mountain fire warning method," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-16, May.
    2. Xinzheng Lu & Donglian Gu & Zhen Xu & Chen Xiong & Yuan Tian, 2020. "CIM-Powered Multi-Hazard Simulation Framework Covering both Individual Buildings and Urban Areas," Sustainability, MDPI, vol. 12(12), pages 1-28, June.
    3. Eric Innocenti & Corinne Idda & Dominique Prunetti & Pierre-Régis Gonsolin, 2022. "Agent-based modelling of a small-scale fishery in Corsica," Post-Print hal-03886619, HAL.
    4. Yuping Tian & Zechuan Wu & Shaojie Bian & Xiaodi Zhang & Bin Wang & Mingze Li, 2022. "Study on Spatial-Distribution Characteristics Based on Fire-Spot Data in Northern China," Sustainability, MDPI, vol. 14(11), pages 1-15, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. da Silva Rocha, André Barreira & Salomão, Gabriel Meyer, 2019. "Environmental policy regulation and corporate compliance in evolutionary game models with well-mixed and structured populations," European Journal of Operational Research, Elsevier, vol. 279(2), pages 486-501.
    2. Wang, Pengcheng & Yu, Guizhen & Wu, Xinkai & Qin, Hongmao & Wang, Yunpeng, 2018. "An extended car-following model to describe connected traffic dynamics under cyberattacks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 351-370.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:nathaz:v:91:y:2018:i:1:d:10.1007_s11069-017-3127-5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.