IDEAS home Printed from https://ideas.repec.org/a/wly/envmet/v33y2022i8ne2774.html
   My bibliography  Save this article

Modeling the spatial evolution wildfires using random spread process

Author

Listed:
  • Carlos Díaz‐Avalos
  • Pablo Juan

Abstract

The study of wildfire spread and the growth of the area burned is an important task in ecological studies and in other contexts. In this work we present a model for fire spread and show the results obtained from simulations of burned areas. The model is based on probabilities of fire at different locations. Such probabilities are obtained from the intensity function of a spatial point process model fitted to the observed pattern of fires in the Valencian Community for the years 1993–2015. The models, applied to different wildfires in Spain, including the different temporal states, combines the features of a network model with those of a quasi‐physical model of the interaction between burning and nonburning cells, which strongly depends on covariates. The results of the simulated wildfire burned areas resemble the burned areas observed in real cases, suggesting that the model proposed, based on a Markov process called Random Spread Process, works adequately. The model can be extended to simulate other random spread processes such as epidemics.

Suggested Citation

  • Carlos Díaz‐Avalos & Pablo Juan, 2022. "Modeling the spatial evolution wildfires using random spread process," Environmetrics, John Wiley & Sons, Ltd., vol. 33(8), December.
  • Handle: RePEc:wly:envmet:v:33:y:2022:i:8:n:e2774
    DOI: 10.1002/env.2774
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/env.2774
    Download Restriction: no

    File URL: https://libkey.io/10.1002/env.2774?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
    ---><---

    References listed on IDEAS

    as
    1. Takaya Saito & Marc Rehmsmeier, 2015. "The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-21, March.
    2. Muzy, A. & Nutaro, J.J. & Zeigler, B.P. & Coquillard, P., 2008. "Modeling and simulation of fire spreading through the activity tracking paradigm," Ecological Modelling, Elsevier, vol. 219(1), pages 212-225.
    3. Yassemi, S. & Dragićević, S. & Schmidt, M., 2008. "Design and implementation of an integrated GIS-based cellular automata model to characterize forest fire behaviour," Ecological Modelling, Elsevier, vol. 210(1), pages 71-84.
    Full references (including those not matched with items on IDEAS)

    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. Christopher J Greenwood & George J Youssef & Primrose Letcher & Jacqui A Macdonald & Lauryn J Hagg & Ann Sanson & Jenn Mcintosh & Delyse M Hutchinson & John W Toumbourou & Matthew Fuller-Tyszkiewicz &, 2020. "A comparison of penalised regression methods for informing the selection of predictive markers," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-14, November.
    2. Le, Hong Hanh & Viviani, Jean-Laurent, 2018. "Predicting bank failure: An improvement by implementing a machine-learning approach to classical financial ratios," Research in International Business and Finance, Elsevier, vol. 44(C), pages 16-25.
    3. João Chang Junior & Fábio Binuesa & Luiz Fernando Caneo & Aida Luiza Ribeiro Turquetto & Elisandra Cristina Trevisan Calvo Arita & Aline Cristina Barbosa & Alfredo Manoel da Silva Fernandes & Evelinda, 2020. "Improving preoperative risk-of-death prediction in surgery congenital heart defects using artificial intelligence model: A pilot study," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-21, September.
    4. Arthur De Sá Ferreira & Ney Meziat-Filho & Ana Paula Antunes Ferreira, 2021. "Double threshold receiver operating characteristic plot for three-modal continuous predictors," Computational Statistics, Springer, vol. 36(3), pages 2231-2245, September.
    5. Gong, Jian-zhou & Liu, Yan-sui & Xia, Bei-cheng & Zhao, Guan-wei, 2009. "Urban ecological security assessment and forecasting, based on a cellular automata model: A case study of Guangzhou, China," Ecological Modelling, Elsevier, vol. 220(24), pages 3612-3620.
    6. Masabho P Milali & Samson S Kiware & Nicodem J Govella & Fredros Okumu & Naveen Bansal & Serdar Bozdag & Jacques D Charlwood & Marta F Maia & Sheila B Ogoma & Floyd E Dowell & George F Corliss & Maggy, 2020. "An autoencoder and artificial neural network-based method to estimate parity status of wild mosquitoes from near-infrared spectra," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-16, June.
    7. Kajal Lahiri & Cheng Yang, 2023. "ROC and PRC Approaches to Evaluate Recession Forecasts," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 19(2), pages 119-148, September.
    8. Tzu-Hsuan Lin & Jehn-Ruey Jiang, 2021. "Credit Card Fraud Detection with Autoencoder and Probabilistic Random Forest," Mathematics, MDPI, vol. 9(21), pages 1-16, October.
    9. Xiaoli Hu & Xin Li & Ling Lu, 2018. "Modeling the Land Use Change in an Arid Oasis Constrained by Water Resources and Environmental Policy Change Using Cellular Automata Models," Sustainability, MDPI, vol. 10(8), pages 1-14, August.
    10. Alfred Krzywicki & David Muchlinski & Benjamin E. Goldsmith & Arcot Sowmya, 2022. "From academia to policy makers: a methodology for real-time forecasting of infrequent events," Journal of Computational Social Science, Springer, vol. 5(2), pages 1489-1510, November.
    11. Dueñas, Marco & Ortiz, Víctor & Riccaboni, Massimo & Serti, Francesco, 2021. "Assessing the Impact of COVID-19 on Trade: a Machine Learning Counterfactual Analysis," Working papers 79, Red Investigadores de Economía.
    12. Hamed Adab & Kasturi Devi Kanniah & Karim Solaimani, 2021. "Remote sensing-based operational modeling of fuel ignitability in Hyrcanian mixed forest, Iran," 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. 108(1), pages 253-283, August.
    13. Jonathan Corcoran & Gary Higgs & David Rohde & Prem Chhetri, 2011. "Investigating the association between weather conditions, calendar events and socio-economic patterns with trends in fire incidence: an Australian case study," Journal of Geographical Systems, Springer, vol. 13(2), pages 193-226, June.
    14. Wei-Hsuan Lo-Ciganic & Julie M Donohue & Eric G Hulsey & Susan Barnes & Yuan Li & Courtney C Kuza & Qingnan Yang & Jeanine Buchanich & James L Huang & Christina Mair & Debbie L Wilson & Walid F Gellad, 2021. "Integrating human services and criminal justice data with claims data to predict risk of opioid overdose among Medicaid beneficiaries: A machine-learning approach," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-18, March.
    15. Nica-Avram, Georgiana & Harvey, John & Smith, Gavin & Smith, Andrew & Goulding, James, 2021. "Identifying food insecurity in food sharing networks via machine learning," Journal of Business Research, Elsevier, vol. 131(C), pages 469-484.
    16. Ali J. Ghandour & Huda Hammoud & Samar Al-Hajj, 2020. "Analyzing Factors Associated with Fatal Road Crashes: A Machine Learning Approach," IJERPH, MDPI, vol. 17(11), pages 1-13, June.
    17. Song, Kwonsik & Anderson, Kyle & Lee, SangHyun, 2020. "An energy-cyber-physical system for personalized normative messaging interventions: Identification and classification of behavioral reference groups," Applied Energy, Elsevier, vol. 260(C).
    18. Fisnik Doko & Slobodan Kalajdziski & Igor Mishkovski, 2021. "Credit Risk Model Based on Central Bank Credit Registry Data," JRFM, MDPI, vol. 14(3), pages 1-17, March.
    19. Abouelmagd THM, 2018. "A New Flexible Distribution Based on the Zero Truncated Poisson Distribution: Mathematical Properties and Applications to Lifetime Data," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 8(1), pages 10-16, August.
    20. Bouvatier, Vincent & El Ouardi, Sofiane, 2023. "Credit gaps as banking crisis predictors: A different tune for middle- and low-income countries," Emerging Markets Review, Elsevier, vol. 54(C).

    More about this item

    Statistics

    Access and download statistics

    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:wly:envmet:v:33:y:2022:i:8:n:e2774. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.interscience.wiley.com/jpages/1180-4009/ .

    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.