Calculation fire risk values of forest areas in marmaris region by using fuzzy set theory

Authors

https://doi.org/10.48313/uda.vi.67

Abstract

Fuzzy Set Theory (FST) is a new theory, introduced as an alternative to classical Set Theory (ST), provides a framework for handling uncertainty and imprecision by allowing membership rather than binary classification. In this study, we propose a novel approach to forest fire risk assessment by utilizing FST to overcome the limitations of classical ST based models. Forest fires cause destruction of thousands of hectares of forestland for Turkey located in the Mediterranean climate zone. As a result of forest fires, entire ecosystem damaged and results show themselves negatively in many dimensions. Therefore, preventing or intervening forest fires is an important situation. Fire risk maps are created in order to prevent forest fires or to be prepared in advance for intervention. Usually, fire risk maps are created with help of equations made by classical ST. However, creating a risk map with classical ST as yes-no may not lead us to exactly the right measures. These created risk maps are not flexible. For this reason, in this study, we aim to determine fire risks from a different perspective with the help of FST and create risk assessment with high efficiency and reality value. Our approach uses FST to assign risk levels based on degrees of membership and aims to allow for more nuanced and realistic risk assessment. By integrating various terrain-related factors into a fuzzy logic framework, it is designed to produce fire risk levels with higher accuracy and better reflect the dynamic nature of fire risk. This method aims to contribute to more effective fire management strategies and minimize potential environmental damage.

Keywords:

Fuzzy set theory, forest fire risk calculation, forest fire risk evaluation in marmaris

References

  1. [1] Küçükosmanoğlu, Y. (2006). Effect of forest fires on the physical and mechanical properties of Brutian pine (Pinus brutia Ten.) wood [Thesis]. (In Turkish).

  2. [2] Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X

  3. [3] Iliadis, L. S. (2005). A decision support system applying an integrated fuzzy model for long-term forest fire risk estimation. Environmental modelling & software, 20(5), 613–621. https://doi.org/10.1016/j.envsoft.2004.03.006

  4. [4] Soto, M. E. C. (2012). The identification and assessment of areas at risk of forest fire using fuzzy methodology. Applied geography, 35(1–2), 199–207. https://doi.org/10.1016/j.apgeog.2012.07.001

  5. [5] Kant Sharma, L., Kanga, S., Singh Nathawat, M., Sinha, S., & Chandra Pandey, P. (2012). Fuzzy AHP for forest fire risk modeling. Disaster prevention and management: an international journal, 21(2), 160–171. https://doi.org/10.1108/09653561211219964

  6. [6] Jafarzadeh, A. A., Mahdavi, A., & Jafarzadeh, H. (2017). Evaluation of forest fire risk using the apriori algorithm and fuzzy c-means clustering. Journal of forest science, 370–380. https://doi.org/10.17221/7/2017-JFS

  7. [7] Eskandari, S. (2017). A new approach for forest fire risk modeling using fuzzy AHP and GIS in Hyrcanian forests of Iran. Arabian journal of geosciences, 10(8), 190. https://doi.org/10.1007/s12517-017-2976-2

  8. [8] Toledo-Castro, J., Caballero-Gil, P., Rodr-guez-Pérez, N., Santos-González, I., Hernández-Goya, C., & Aguasca-Colomo, R. (2018). Forest fire prevention, detection, and fighting based on fuzzy logic and wireless sensor networks. Complexity, 2018(1), 1639715. https://doi.org/10.1155/2018/1639715

  9. [9] Abedi Gheshlaghi, H., Feizizadeh, B., & Blaschke, T. (2020). GIS-based forest fire risk mapping using the analytical network process and fuzzy logic. Journal of environmental planning and management, 63(3), 481–499. https://doi.org/10.1080/09640568.2019.1594726

  10. [10] Demir, A., & Akay, A. E. (2024). Forest fire risk mapping using gis based analytical hierarchy process approach. European journal of forest engineering, 10(1), 15–28. https://doi.org/10.33904/ejfe.1400233

  11. [11] Uçar, Z., Güney, C. O., Akay, A. E., Bilici, E., & Erkan, N. (2025). Mapping the probability of Forest fire in the mediterranean region of Türkiye using the GIS-based fuzzy-AHP method. Human and ecological risk assessment: an international journal, 31(1–2), 234–259. https://doi.org/10.1080/10807039.2025.2451146

  12. [12] Erten, E., Kurgun, V., & Musaouglu, N. (2005). Uzaktan algılama ve CBS kullanarak orman yangını bilgi sisteminin kurulması [Establishing a forest fire information system using remote sensing and GIS]. TMMOB Harita ve Kadastro Mühendisleri Odası 10. Türkiye Harita Bilimsel ve Teknik Kurultayı, Ankara, Türkiye. https://obs.hkmo.org.tr/show-media/resimler/ekler/NDKO_109_ek.pdf

  13. [13] Joaquim, G. S., Bahaaeddin, A., & Josep, R. C. (2007). Remote sensing analysis to detect fire risk locations. GéoCongrès-2007, québec, canada. https://www.oicrf.org/-/remote-sensing-analysis-to-detect-fire-risk-locations

  14. [14] Bilgili, E. (2014). Provisional Lecture Notes for Forestry Conservation Course. (In Turkish). ktu.edu.tr/dosyalar/15_01_02_c2f03.pdf

  15. [15] Güngöroğlu, C. (2017). Determination of forest fire risk with fuzzy analytic hierarchy process and its mapping with the application of GIS: The case of Turkey/Çakırlar. Human and ecological risk assessment: an international journal, 23(2), 388–406. https://doi.org/10.1080/10807039.2016.1255136

  16. [16] Fairbrother, A., & Turnley, J. G. (2005). Predicting risks of uncharacteristic wildfires: Application of the risk assessment process. Forest ecology and management, 211(1–2), 28–35. https://doi.org/10.1016/j.foreco.2005.01.026

  17. [17] Nesterov, V. G. (1949). Combustibility of the forest and methods for its determination. USSR State Industry Press Moscow. https://sibran.ru/upload/medialibrary/Forest_NV.pdf

  18. [18] Alkayış, M. H., Karslıoğlu, A. & Onur, M. İ. (2022). Determining the forest fire risk potential map of the Menteşe region in Muğla province using geographic information systems. Geomatik, 7(1), 10–16. (In Turkish). https://doi.org/10.29128/geomatik.791545

  19. [19] Baltaci, U., & Yildirim, F. (2021). Multi-criteria analysis and mapping of forest fire risk in Mugla Regional Directorate of Forestry. Turkish Journal of Forestry Research, 8(1), 1–11.

  20. [20] Avila-Flores, D., Pompa-Garcia, M., Antonio-Nemiga, X., Rodriguez-Trejo, D. A., Vargas-Perez, E., & Santillan-Perez, J. (2010). Driving factors for forest fire occurrence in Durango State of Mexico: A geospatial perspective. Chinese geographical science, 20(6), 491–497. https://doi.org/10.1007/s11769-010-0437-x

  21. [21] Karabulut, M., Karakoç, A, Gürbüz, M. & Kızılelma, Y. (2013). Determining forest fire risk areas in Başkonuş Mountain (Kahramanmaraş) using geographic information systems. Uluslararası Sosyal Araştırmalar Dergisi, 6(24), 171–179. https://www.academia.edu/25985542/Baskonus_daginda_orman_yangin_analizi

  22. [22] Marmaris Governorship. Mahalli idareler [Local administrations]. 96589. http://www.marmaris.gov.tr/mahalli-idareler

  23. [23] Geometric Information Systems. Vatandaş [Citizen]. https://cbs.ogm.gov.tr/vatandas/

  24. [24] Marmaris City Automation System. (2024). KEOS: Elektronik ve Elektronik Atık Yönetim Sistemi. https://keos.marmaris.bel.tr:4433/keos/

  25. [25] Republic of Turkey Ministry of Culture and Tourism. Map of Marmaris. https://mugla.ktb.gov.tr/TR-270726/marmaris.html

  26. [26] Republic of Turkey Ministry of Environment, Urbanization and Climate Change. Muğla mülk idare il haritası. https://www.harita.gov.tr/uploads/files/products/mugla-mulk-idare-il-haritasi-1367.pdf

  27. [27] Bingöl, B. (2017). Determination of forest fire risk areas in Burdur Province using Geographical Information Systems. Turkish journal of forest science, 1(2), 169–182. https://doi.org/10.32328/turkjforsci.319155

  28. [28] Şengönül, K. (1985). The Relationship Between Forest Fires and Soil Warming, and the Effects of Fires on Soil Properties. Journal of the faculty of forestry istanbul university, 35(2), 99–107. https://dergipark.org.tr/en/download/article-file/175746

  29. [29] Erdemli, F. (2023). Orman yanginlari değerlendirme raporu. https://www.ogm.gov.tr/muglaobm/duyurular-sitesi/Documents/mugla-obm-2023-yili-yangin-degerlendirme-raporu/2023+Yili+Orman+Yanginlari+Degerlendirme+Raporu.pdf

  30. [30] Coşandali, M., & Partigöç, N. S. (2022). The Impact of Disasters on Tourism Sector: The Case of Forest Fires in Mugla-Marmaris District. Resilience, 6(2), 257–267. https://doi.org/10.32569/resilience.1211459

  31. [31] Kavgacı, A., & Başararan, M. A. (2023). Orman yanginlari. https://www.ormancilardernegi.org/Documents/24c06fcf-500e-4b5c-90a3-163f5e62b0d6.pdf

Published

2025-12-23

How to Cite

Yaylalı Umul, G., & Akdeniz, T. (2025). Calculation fire risk values of forest areas in marmaris region by using fuzzy set theory. Uncertainty Discourse and Applications, 2(4), 318-330. https://doi.org/10.48313/uda.vi.67

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