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GIS-based forest fire risk mapping using the analytical network process and fuzzy logic

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  • Hassan Abedi Gheshlaghi
  • Bakhtiar Feizizadeh
  • Thomas Blaschke

Abstract

This research investigates the efficiency of combining the Analytical Network Process (ANP) and fuzzy logic for developing a fire risk map. Major factors influencing the occurrence of forest fires were identified from the literature. The importance of each factor was determined by an ANP ranking procedure, which yielded the criteria weights, while fuzzy logic was employed for assessing the weights of the subcriteria. Then, GIS-based aggregation functions were employed to produce a fire risk map. In order to validate the results, forest fire locations were identified using field data, satellite images, and national reports. This validation revealed a very high accuracy of 0.819 for the fuzzy ANP model. The results will serve as guidelines for researchers and scientists by introducing new and robust MCDA methods. In general, the mentioned Hybrid method can be applied to early warning, fire suppression resources planning, and allocation work in the study area.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:jenpmg:v:63:y:2020:i:3:p:481-499
    DOI: 10.1080/09640568.2019.1594726
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    Cited by:

    1. 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.
    2. Hazem Ghassan Abdo & Hussein Almohamad & Ahmed Abdullah Al Dughairi & Motirh Al-Mutiry, 2022. "GIS-Based Frequency Ratio and Analytic Hierarchy Process for Forest Fire Susceptibility Mapping in the Western Region of Syria," Sustainability, MDPI, vol. 14(8), pages 1-20, April.
    3. 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.
    4. 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.
    5. Shu Wu, 2021. "The Temporal-Spatial Distribution and Information-Diffusion-Based Risk Assessment of Forest Fires in China," Sustainability, MDPI, vol. 13(24), pages 1-18, December.
    6. Mustafa Topuz & Mehmet Deniz, 2023. "Application of GIS and AHP for land use suitability analysis: case of Demirci district (Turkey)," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-15, December.
    7. Silvia Rolandi & Gianluca Brunori & Manlio Bacco & Ivano Scotti, 2021. "The Digitalization of Agriculture and Rural Areas: Towards a Taxonomy of the Impacts," Sustainability, MDPI, vol. 13(9), pages 1-16, May.
    8. Parviz Mohamadzadeh & Samereh Pourmoradian & Bakhtiar Feizizadeh & Ayyoob Sharifi & Mathias Vogdrup-Schmidt, 2020. "A GIS-Based Approach for Spatially-Explicit Sustainable Development Assessments in East Azerbaijan Province, Iran," Sustainability, MDPI, vol. 12(24), pages 1-16, December.
    9. 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.

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