IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i7p3881-d779418.html
   My bibliography  Save this article

Wildfire Risk Forecasting Using Weights of Evidence and Statistical Index Models

Author

Listed:
  • Ghafar Salavati

    (Faculty of Rangeland and Watershed Management, Gorgan University of Agricultural Sciences and Natural Resources, Shahid Beheshti St Central Organization of the University, Gorgan 4913815739, Iran)

  • Ebrahim Saniei

    (Faculty of Rangeland and Watershed Management, Gorgan University of Agricultural Sciences and Natural Resources, Shahid Beheshti St Central Organization of the University, Gorgan 4913815739, Iran)

  • Ebrahim Ghaderpour

    (Department of Geomatics Engineering, Schulich School of Engineering, University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, Canada)

  • Quazi K. Hassan

    (Department of Geomatics Engineering, Schulich School of Engineering, University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, Canada)

Abstract

The risk of forest and pasture fires is one of the research topics of interest around the world. Applying precise strategies to prevent potential effects and minimize the occurrence of such incidents requires modeling. This research was conducted in the city of Sanandaj, which is located in the west of the province of Kurdistan and the west of Iran. In this study, fire risk potential was assessed using weights of evidence (WoE) and statistical index (SI) models. Information about fire incidents in Sanandaj (2011–2020) was divided into two parts: educational data (2011–2017) and validation data (2018–2020). Factors considered for potential forest and rangeland fire risk in Sanandaj city included altitude, slope percentage, slope direction, distance from the road, distance from the river, land use/land cover (LULC), average annual rainfall, and average annual temperature. Finally, in order to validate the two models used, the receiver operating characteristic (ROC) curve was used. The results for the WoE and SI models showed that about 62.96% and 52.75% of the study area, respectively, were in the moderate risk to very high risk classes. In addition, the results of the ROC curve analysis showed that the WoE and SI models had area under the curve (AUC) values of 0.741 and 0.739, respectively. Although the input parameters for both models were the same, the WoE model showed a slightly higher AUC value compared to the SI model, and can potentially be used to predict future fire risk in the study area. The results of this study can help decision makers and managers take the necessary precautions to prevent forest and rangeland fires and/or to minimize fire damage.

Suggested Citation

  • Ghafar Salavati & Ebrahim Saniei & Ebrahim Ghaderpour & Quazi K. Hassan, 2022. "Wildfire Risk Forecasting Using Weights of Evidence and Statistical Index Models," Sustainability, MDPI, vol. 14(7), pages 1-15, March.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:7:p:3881-:d:779418
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/7/3881/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/14/7/3881/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lorena Liuzzo & Vincenzo Sammartano & Gabriele Freni, 2019. "Comparison between Different Distributed Methods for Flood Susceptibility Mapping," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(9), pages 3155-3173, July.
    2. Amatulli, Giuseppe & Peréz-Cabello, Fernando & de la Riva, Juan, 2007. "Mapping lightning/human-caused wildfires occurrence under ignition point location uncertainty," Ecological Modelling, Elsevier, vol. 200(3), pages 321-333.
    3. Keane, Robert E. & Drury, Stacy A. & Karau, Eva C. & Hessburg, Paul F. & Reynolds, Keith M., 2010. "A method for mapping fire hazard and risk across multiple scales and its application in fire management," Ecological Modelling, Elsevier, vol. 221(1), pages 2-18.
    4. Hamed Adab & Kasturi Kanniah & Karim Solaimani, 2013. "Modeling forest fire risk in the northeast of Iran using remote sensing and GIS techniques," 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. 65(3), pages 1723-1743, February.
    5. Shruti Sachdeva & Tarunpreet Bhatia & A. K. Verma, 2018. "GIS-based evolutionary optimized Gradient Boosted Decision Trees for forest fire susceptibility mapping," 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. 92(3), pages 1399-1418, July.
    6. Hassan Abedi Gheshlaghi & Bakhtiar Feizizadeh & Thomas Blaschke, 2020. "GIS-based forest fire risk mapping using the analytical network process and fuzzy logic," Journal of Environmental Planning and Management, Taylor & Francis Journals, vol. 63(3), pages 481-499, February.
    7. Minerva Singh & Zhuhua Huang, 2022. "Analysis of Forest Fire Dynamics, Distribution and Main Drivers in the Atlantic Forest," Sustainability, MDPI, vol. 14(2), pages 1-15, January.
    8. Naderpour, Mohsen & Rizeei, Hossein Mojaddadi & Khakzad, Nima & Pradhan, Biswajeet, 2019. "Forest fire induced Natech risk assessment: A survey of geospatial technologies," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    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. Osama Ashraf Mohammed & Sasan Vafaei & Mehdi Mirzaei Kurdalivand & Sabri Rasooli & Chaolong Yao & Tongxin Hu, 2022. "A Comparative Study of Forest Fire Mapping Using GIS-Based Data Mining Approaches in Western Iran," Sustainability, MDPI, vol. 14(20), pages 1-13, October.
    2. José Francisco de Oliveira-Júnior & Munawar Shah & Ayesha Abbas & Washington Luiz Félix Correia Filho & Carlos Antonio da Silva Junior & Dimas de Barros Santiago & Paulo Eduardo Teodoro & David Mendes, 2022. "Spatiotemporal Analysis of Fire Foci and Environmental Degradation in the Biomes of Northeastern Brazil," Sustainability, MDPI, vol. 14(11), pages 1-19, June.
    3. Ayesha Maqbool & Alina Mirza & Farkhanda Afzal & Tajammul Shah & Wazir Zada Khan & Yousaf Bin Zikria & Sung Won Kim, 2022. "System-Level Performance Analysis of Cooperative Multiple Unmanned Aerial Vehicles for Wildfire Surveillance Using Agent-Based Modeling," Sustainability, MDPI, vol. 14(10), pages 1-21, May.
    4. Ebrahim Ghaderpour & Paolo Mazzanti & Francesca Bozzano & Gabriele Scarascia Mugnozza, 2024. "Trend Analysis of MODIS Land Surface Temperature and Land Cover in Central Italy," Land, MDPI, vol. 13(6), 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. Sarkawt G. Salar & Arsalan Ahmed Othman & Sabri Rasooli & Salahalddin S. Ali & Zaid T. Al-Attar & Veraldo Liesenberg, 2022. "GIS-Based Modeling for Vegetated Land Fire Prediction in Qaradagh Area, Kurdistan Region, Iraq," Sustainability, MDPI, vol. 14(10), pages 1-31, May.
    2. Osama Ashraf Mohammed & Sasan Vafaei & Mehdi Mirzaei Kurdalivand & Sabri Rasooli & Chaolong Yao & Tongxin Hu, 2022. "A Comparative Study of Forest Fire Mapping Using GIS-Based Data Mining Approaches in Western Iran," Sustainability, MDPI, vol. 14(20), pages 1-13, October.
    3. Naderpour, Mohsen & Rizeei, Hossein Mojaddadi & Khakzad, Nima & Pradhan, Biswajeet, 2019. "Forest fire induced Natech risk assessment: A survey of geospatial technologies," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    4. Dingli Liu & Zhisheng Xu & Chuangang Fan, 2019. "Predictive analysis of fire frequency based on daily temperatures," 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. 97(3), pages 1175-1189, July.
    5. 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.
    6. Polash Banerjee, 2022. "MODIS-FIRMS and ground-truthing-based wildfire likelihood mapping of Sikkim Himalaya using machine learning algorithms," 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. 110(2), pages 899-935, January.
    7. Ricci, Federica & Misuri, Alessio & Scarponi, Giordano Emrys & Cozzani, Valerio & Demichela, Micaela, 2024. "Vulnerability Assessment of Industrial Sites to Interface Fires and Wildfires," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    8. Chaoxue Tan & Zhongke Feng, 2023. "Mapping Forest Fire Risk Zones Using Machine Learning Algorithms in Hunan Province, China," Sustainability, MDPI, vol. 15(7), pages 1-17, April.
    9. Mohammad Reza Mansouri Daneshvar & Friedemann T. Freund & Majid Ebrahimi, 2023. "Spatial and Temporal Analysis of Climatic Precursors before Major Earthquakes in Iran (2011–2021)," Sustainability, MDPI, vol. 15(14), pages 1-30, July.
    10. Kuter, Semih & Usul, Nurunnisa & Kuter, Nazan, 2011. "Bandwidth determination for kernel density analysis of wildfire events at forest sub-district scale," Ecological Modelling, Elsevier, vol. 222(17), pages 3033-3040.
    11. Abdulwaheed Tella & Abdul-Lateef Balogun, 2020. "Ensemble fuzzy MCDM for spatial assessment of flood susceptibility in Ibadan, Nigeria," 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(3), pages 2277-2306, December.
    12. Seidl, Rupert & Fernandes, Paulo M. & Fonseca, Teresa F. & Gillet, François & Jönsson, Anna Maria & Merganičová, Katarína & Netherer, Sigrid & Arpaci, Alexander & Bontemps, Jean-Daniel & Bugmann, Hara, 2011. "Modelling natural disturbances in forest ecosystems: a review," Ecological Modelling, Elsevier, vol. 222(4), pages 903-924.
    13. Qiang Hu & Yuelong Zhu & Hexuan Hu & Zhuang Guan & Zeyu Qian & Aiming Yang, 2022. "Multiple Kernel Learning with Maximum Inundation Extent from MODIS Imagery for Spatial Prediction of Flood Susceptibility," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(1), pages 55-73, January.
    14. Samereh Pourmoradian & Ali Vandshoari & Davoud Omarzadeh & Ayyoob Sharifi & Naser Sanobuar & Seyyed Samad Hosseini, 2021. "An Integrated Approach to Assess Potential and Sustainability of Handmade Carpet Production in Different Areas of the East Azerbaijan Province of Iran," Sustainability, MDPI, vol. 13(4), pages 1-21, February.
    15. Hamed Adab, 2017. "Landfire hazard assessment in the Caspian Hyrcanian forest ecoregion with the long-term MODIS active fire data," 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. 87(3), pages 1807-1825, July.
    16. Wang, Ning & Zhao, Shiyue & Wang, Sutong, 2024. "A novel clustering-based resampling with cost-sensitive boosting method to model and map wildfire susceptibility," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    17. Wang, Ning & Xu, Yan & Wang, Sutong, 2022. "Interpretable boosting tree ensemble method for multisource building fire loss prediction," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    18. Xu, Zhaoyi & Saleh, Joseph Homer, 2021. "Machine learning for reliability engineering and safety applications: Review of current status and future opportunities," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    19. Shruti Sachdeva & Tarunpreet Bhatia & A. K. Verma, 2018. "GIS-based evolutionary optimized Gradient Boosted Decision Trees for forest fire susceptibility mapping," 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. 92(3), pages 1399-1418, July.
    20. James Gaboardi, 2020. "Validating Abstract Representations of Spatial Population Data while considering Disclosure Avoidance," Working Papers 20-5, Center for Economic Studies, U.S. Census Bureau.

    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:gam:jsusta:v:14:y:2022:i:7:p:3881-:d:779418. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.