IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v119y2023i1d10.1007_s11069-023-06133-4.html
   My bibliography  Save this article

Effect of climate change on fire regimes in natural resources of northern Iran: investigation of spatiotemporal relationships using regression and data mining models

Author

Listed:
  • Saeedeh Eskandari

    (Agricultural Research Education and Extension Organization (AREEO))

  • Hooman Ravanbakhsh

    (Agricultural Research Education and Extension Organization (AREEO))

  • Yazdanfar Ahangaran

    (Natural Resources and Watershed Organization of Iran)

  • Zolfaghar Rezapour

    (Kohgiluyeh and Boyer Ahmad Meteorological Administration)

  • Hamid Reza Pourghasemi

    (Shiraz University)

Abstract

Mazandaran province in northern Iran is one of the fire-prone areas in the country in which a wide area of its natural resources have been destroyed by fire in recent years. This research aimed to detect the spatiotemporal relationships between climatic variables and fire regimes in Mazandaran province in recent decades. The fire variables (dependent variables) were the number and area of fires. The climatic variables (independent variables) were seasonal temperature mean, seasonal maximum temperature mean, seasonal absolute maximum temperature, seasonal precipitation mean, seasonal relative humidity mean, seasonal wind speed mean, and seasonal maximum wind speed mean for 26 years (1996–2021). Pearson's correlation coefficient and regression models were used to investigate the temporal relationship between fire and climatic variables during study period. Data mining models were used to detect the spatial relationship between fire ignition and climatic parameters and to produce the fire danger maps. The fire occurrence map was obtained from Mazandaran Natural Resources and Watershed Administration and Moderate-Resolution Imaging Spectroradiometer (MODIS) sensor. The climatic maps were obtained by interpolation methods in GIS. The weight of climatic parameters in fire ignition was determined using MDG and MDA statistics from random forest (RF) algorithm. Then different data mining models (logistic regression, random forest, support vector machine, and SVM-RF ensemble model) and 70% of actual fires were used for modeling fire danger in R software. The area under the curve and 30% of actual fires were applied for accuracy assessment of the models. Results of temporal relationships indicated that there are significant relationships among the number of fires and seasonal absolute maximum temperature, seasonal precipitation mean, and seasonal relative humidity mean. On the other hand, a significant relationship was observed between the area of fires and seasonal temperature mean. The results of spatial relationship demonstrated that seasonal temperature mean, seasonal precipitation mean, and seasonal relative humidity mean had the greatest spatial importance in fire ignition. The results of accuracy assessment of fire danger models indicated that SVM-RF and RF models were the best models for fire danger mapping. Therefore, using the maps obtained from these models, it is possible to predict the climate-caused fires in natural ecosystems of Mazandaran province.

Suggested Citation

  • Saeedeh Eskandari & Hooman Ravanbakhsh & Yazdanfar Ahangaran & Zolfaghar Rezapour & Hamid Reza Pourghasemi, 2023. "Effect of climate change on fire regimes in natural resources of northern Iran: investigation of spatiotemporal relationships using regression and data mining models," 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. 119(1), pages 497-521, October.
  • Handle: RePEc:spr:nathaz:v:119:y:2023:i:1:d:10.1007_s11069-023-06133-4
    DOI: 10.1007/s11069-023-06133-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-023-06133-4
    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/s11069-023-06133-4?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. Chao Song & Mei-Po Kwan & Weiguo Song & Jiping Zhu, 2017. "A Comparison between Spatial Econometric Models and Random Forest for Modeling Fire Occurrence," Sustainability, MDPI, vol. 9(5), pages 1-21, May.
    2. Philip E Higuera & John T Abatzoglou & Jeremy S Littell & Penelope Morgan, 2015. "The Changing Strength and Nature of Fire-Climate Relationships in the Northern Rocky Mountains, U.S.A., 1902-2008," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-21, June.
    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. Asma Shaheen & Javed Iqbal, 2018. "Spatial Distribution and Mobility Assessment of Carcinogenic Heavy Metals in Soil Profiles Using Geostatistics and Random Forest, Boruta Algorithm," Sustainability, MDPI, vol. 10(3), pages 1-20, March.
    2. Alexandra D Syphard & Timothy Sheehan & Heather Rustigian-Romsos & Kenneth Ferschweiler, 2018. "Mapping future fire probability under climate change: Does vegetation matter?," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-23, August.
    3. Diana R. Gergel & Bart Nijssen & John T. Abatzoglou & Dennis P. Lettenmaier & Matt R. Stumbaugh, 2017. "Effects of climate change on snowpack and fire potential in the western USA," Climatic Change, Springer, vol. 141(2), pages 287-299, March.
    4. Abdullah Al Saim & Mohamed H. Aly, 2022. "Machine Learning for Modeling Wildfire Susceptibility at the State Level: An Example from Arkansas, USA," Geographies, MDPI, vol. 2(1), pages 1-17, January.
    5. Dawid Siwicki, 2021. "The Application of Machine Learning Algorithms for Spatial Analysis: Predicting of Real Estate Prices in Warsaw," Working Papers 2021-05, Faculty of Economic Sciences, University of Warsaw.
    6. Richard Waring & Nicholas Coops, 2016. "Predicting large wildfires across western North America by modeling seasonal variation in soil water balance," Climatic Change, Springer, vol. 135(2), pages 325-339, March.
    7. Saeedeh Eskandari & Mahdis Amiri & Nitheshnirmal Sãdhasivam & Hamid Reza Pourghasemi, 2020. "Comparison of new individual and hybrid machine learning algorithms for modeling and mapping fire hazard: a supplementary analysis of fire hazard in different counties of Golestan Province in 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. 104(1), pages 305-327, October.
    8. Jiansong Wu & Zhuqiang Hu & Jinyue Chen & Zheng Li, 2018. "Risk Assessment of Underground Subway Stations to Fire Disasters Using Bayesian Network," Sustainability, MDPI, vol. 10(10), pages 1-21, October.
    9. Henne, Paul D. & Hawbaker, Todd J., 2023. "An aridity threshold model of fire sizes and annual area burned in extensively forested ecoregions of the western USA," Ecological Modelling, Elsevier, vol. 477(C).
    10. Richard H. Waring & Nicholas C. Coops, 2016. "Predicting large wildfires across western North America by modeling seasonal variation in soil water balance," Climatic Change, Springer, vol. 135(2), pages 325-339, March.

    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:119:y:2023:i:1:d:10.1007_s11069-023-06133-4. 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.