SavedAdd your journal

Journals / Gazi University Journal of Science / 2026 / Volume: 39 Issue: 3

Prioritisation of Criteria in the Analysis of Forest Fire Vulnerable Areas by Interval Valued Neutrosophic AHP and Spherical Fuzzy AHP Methods

Orman Yangını Riskli Alanların Analizinde Aralıklı Değerli Nötrosofik AHP ve Küresel Bulanık AHP Yöntemlerine Göre Kriterlerin Önceliklendirilmesi

Burcu Tezcan(KAPADOKYA ÜNİVERSİTESİ)0000-0002-0997-7761
Tamer Eren(KIRIKKALE UNIV)0000-0001-5282-3138
Pages
—
Publication Date
2026-09-01
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

This shows the consistency of the two methods, which reflects the reality of wildfires.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Wildfires arise from multiple causes and can spread rapidly across large areas, posing severe environmental challenges.
📊
Key Findings & Data:In Turkey, nearly 12 million hectares predominantly located in the Aegean and Mediterranean regions are considered highly susceptible to such events.
💡
Conclusion & Impact:This shows the consistency of the two methods, which reflects the reality of wildfires.
🔬Design:Review & Systematic Survey
📍Scope:Comprehensive Sample
⚙️Variables:Forest Fires · Climate change impact · IVN-AHP & SF-AHP

Abstract

Wildfires arise from multiple causes and can spread rapidly across large areas, posing severe environmental challenges. In Turkey, nearly 12 million hectares predominantly located in the Aegean and Mediterranean regions are considered highly susceptible to such events. Vulnerable zones are characterized by areas where ignition can occur easily and where flames can quickly propagate to surrounding landscapes. Given limited control over natural dynamics, this study focuses on prioritizing the criteria that influence wildfire vulnerability. Based on expert consultations and a comprehensive literature review, five main categories and seventeen sub-criteria were established. The primary groups comprise climatic and meteorological factors, vegetation characteristics, geographical conditions, human-related activities, and remote sensing indicators. The detailed sub-criteria include variables such as temperature, humidity, precipitation, flammable vegetation, forest density, dried biomass, slope, aspect, coastal proximity, soil properties, settlement concentration, agricultural and husbandry practices, tourism, industrial activity and power lines, historical fire records, drought indices, and afforestation practices. All of these criteria play an important role in the probability of wildfires. For the first time, these parameters were weighted with the Interval-Valued Neutrosophic AHP (IVN-AHP) and Spherical Fuzzy AHP (SF-AHP) methods. It was identified that, among all the factors, humidity was the most critical parameter, with weights of 9.47% and 11.07% for IVN-AHP and SF-AHP, respectively. It has been observed that low humidity levels lead to the drying of vegetation, which becomes highly prone to wildfires, even with small ignition sources. This shows the consistency of the two methods, which reflects the reality of wildfires.

Özet

Orman yangınları farklı nedenlerle ortaya çıkabilir ve geniş alanlara hızla yayılarak önemli bir çevre sorunu oluşturabilir. Türkiye'de, özellikle Ege ve Akdeniz bölgelerinde yaklaşık 12 milyon hektarlık alan orman yangını riski altındadır. Risk bölgeleri, yangınların kolayca başlayıp çevreye hızla yayılma potansiyeline sahip alanları ifade eder. Doğal süreçler üzerinde tam bir kontrol sağlamak zordur. Bu doğrultuda, bu çalışma orman yangını hassas alanlarının analizinde kriterlerin önceliklendirilmesi sorununu ele almaktadır. Uzman ekip ve literatürdeki çalışmalar sonucu 5 ana kriter ve 17 alt kriter belirlenmiştir. Ana kriterler; İklim ve hava koşulları, Bitki örtüsü, Coğrafi özellikler, İnsan faaliyetleri ve Meteorolojik ve uzaktan algılamadır. Alt kriterler Sıcaklık, Nem, Yağış, Yanıcı bitki türleri, Yoğun ormanlık alanlar, Kurumuş bitki örtüsü, Eğim, Yön, Deniz kıyısına yakınlık, Toprak yapısı, Yerleşim yoğunluğu, Tarım ve hayvancılık, Turizm, Sanayi ve elektrik hatları, Tarihi orman yangını verileri, Kuraklık indeksi ve Ağaçlandırma'dır. Bu kriterler orman yangını olasılığında önemli rol oynar. Bu kriterler ilk kez IVN-AHP ve SF-AHP yöntemleri kullanılarak sıralanmıştır. Nem, IVN-AHP yönteminde %9,47 ve SF-AHP yönteminde %11,07 ile ilk sırada yer almaktadır. Nem azaldığında bitki örtüsü kuru ve yanıcı hale gelir. Bu durum en küçük kıvılcımın bile büyük ölçekli bir orman yangınına dönüşmesi için zemin hazırlar. Her iki yöntemin sonuçları gerçek dünya durumuyla tutarlıdır.

🔖

Cite This Article

Tezcan, B., Eren, T. (2026). Prioritisation of Criteria in the Analysis of Forest Fire Vulnerable Areas by Interval Valued Neutrosophic AHP and Spherical Fuzzy AHP Methods. Gazi University Journal of Science, Volume: 39 Issue: 3. https://doi.org/10.35378/gujs.1777308

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (54)

  1. Rigolot, E., “Impact du changement climatique sur les feux de forêté”, Forêt Méditerranéenne, 29(2): 167-176, (2008). DOI: https://hal.science/hal-03573294v1
  2. El Mazi, M., Boutallaka, M., Saber, E. R., Chanyour, Y., and Bouhlal, A., “Forest fire risk modeling in Mediterranean forests using GIS and AHP method: case of the high Rif forest massif (Morocco)”, Euro-Mediterranean Journal for Environmental Integration, 9(3): 1109-1123, (2024). DOI: https://doi.org/10.1007/s41207-024-00591-3
  3. Busico, G., Giuditta, E., Kazakis, N., and Colombani, N., “A hybrid GIS and AHP approach for modelling actual and future forest fire risk under climate change accounting water resources attenuation role”, Sustainability, 11(24): 7166, (2019). DOI: https://doi.org/10.3390/su11247166
  4. Fernández-García, V., Fulé, P. Z., Marcos, E., and Calvo, L., “The role of fire frequency and severity on the regeneration of Mediterranean serotinous pines under different environmental conditions”, Forest Ecology and Management, 444: 59-68, (2019). DOI: https://doi.org/10.1016/j.foreco.2019.04.040
  5. Driouech, F., ElRhaz, K., Moufouma-Okia, W., Arjdal, K., and Balhane, S., “Assessing future changes of climate extreme events in the CORDEX-MENA region using regional climate model ALADIN-climate”, Earth Systems and Environment, 4(3): 477-492, (2020). DOI: https://doi.org/10.1007/s41748-020-00169-3
  6. Moreno, M., Bertolín, C., Arlanzón, D., Ortiz, P., and Ortiz, R., “Climate change, large fires, and cultural landscapes in the mediterranean basin: An analysis in southern Spain”, Heliyon, 9(6), (2023). DOI: https://doi.org/10.1016/j.heliyon.2023.e16941
  7. Francos, M., Úbeda, X., Tort, J., Panareda, J. M., and Cerdà, A., “The role of forest fire severity on vegetation recovery after 18 years. Implications for forest management of Quercus suber L. in Iberian Peninsula”, Global and Planetary Change, 145: 11-16, (2016). DOI: https://doi.org/10.1016/j.gloplacha.2016.07.016
  8. Novo, A., Fariñas-Álvarez, N., Martínez-Sánchez, J., González-Jorge, H., Fernández-Alonso, J. M., and Lorenzo, H., “Mapping forest fire risk—a case study in Galicia (Spain)”, Remote Sensing, 12(22): 3705, (2020). DOI: https://doi.org/10.3390/rs12223705
  9. Francos, M., Ubeda, X., and Pereira, P., “Impact of torrential rainfall and salvage logging on post-wildfire soil properties in NE Iberian Peninsula”, Catena, 177: 210-218, (2019). DOI: https://doi.org/10.1016/j.catena.2019.02.014
  10. de Pagter, T., Lucas-Borja, M. E., Navidi, M., Carra, B. G., Baartman, J., and Zema, D. A., “Effects of wildfire and post-fire salvage logging on rainsplash erosion in a semi-arid pine forest of Central Eastern Spain”, Journal of Environmental Management, 329, 117059, (2023). DOI: https://doi.org/10.1016/j.jenvman.2022.117059
  11. Pereira, P., Francos, M., Brevik, E. C., Ubeda, X., and Bogunovic, I., “Post-fire soil management”, Current Opinion in Environmental Science and Health, 5: 26-32, (2018). DOI: https://doi.org/10.1016/j.coesh.2018.04.002
  12. Vieira, D. C. S., Borrelli, P., Jahanianfard, D., Benali, A., Scarpa, S., and Panagos, P., “Wildfires in Europe: Burned soils require attention”, Environmental Research, 217, 114936, (2023). DOI: https://doi.org/10.1016/j.envres.2022.114936
  13. Rodrigues, M., and De la Riva, J., “An insight into machine-learning algorithms to model human-caused wildfire occurrence”, Environmental Modelling and Software, 57: 192-201, (2014). DOI: https://doi.org/10.1016/j.envsoft.2014.03.003
  14. de Bem, P. P., de Carvalho Júnior, O. A., Matricardi, E. A. T., Guimarães, R. F., and Gomes, R. A. T., “Predicting wildfire vulnerability using logistic regression and artificial neural networks: a case study in Brazil’s Federal District”, International Journal of Wildland Fire, 28(1): 35-45, (2019). DOI: https://doi.org/10.1071/WF18018
  15. Nami, M. H., Jaafari, A., Fallah, M., and Nabiuni, S., “Spatial prediction of wildfire probability in the Hyrcanian ecoregion using evidential belief function model and GIS”, International Journal of Environmental Science and Technology, 15(2): 373-384, (2018). DOI: https://doi.org/10.1007/s13762-017-1371-6
  16. Jaafari, A., Zenner, E. K., Panahi, M., and Shahabi, H., “Hybrid artificial intelligence models based on a neuro-fuzzy system and metaheuristic optimization algorithms for spatial prediction of wildfire probability”, Agricultural and Forest Meteorology, 266: 198-207, (2019). DOI: https://doi.org/10.1016/j.agrformet.2018.12.015
  17. Kim, S. J., Lim, C. H., Kim, G. S., Lee, J., Geiger, T., Rahmati, O., and Lee, W. K., “Multi-temporal analysis of forest fire probability using socio-economic and environmental variables”, Remote Sensing, 11(1): 86, (2019). DOI: https://doi.org/10.3390/rs11010086
  18. Rihan, W., Zhao, J., Zhang, H., Guo, X., Ying, H., Deng, G., and Li, H., “Wildfires on the Mongolian Plateau: Identifying drivers and spatial distributions to predict wildfire probability”, Remote Sensing, 11(20): 2361, (2019). DOI: https://doi.org/10.3390/rs11202361
  19. Galizia, L. F. D. C., and Rodrigues, M., “Modeling the influence of eucalypt plantation on wildfire occurrence in the Brazilian savanna biome”, Forests, 10(10): 844, (2019). DOI: https://doi.org/10.3390/f10100844
  20. Jaafari, A., Termeh, S. V. R., and Bui, D. T., “Genetic and firefly metaheuristic algorithms for an optimized neuro-fuzzy prediction modeling of wildfire probability”, Journal of Environmental Management, 243: 358-369, (2019). DOI: https://doi.org/10.1016/j.jenvman.2019.04.117
  21. Sayad, Y. O., Mousannif, H., and Al Moatassime, H., “Predictive modeling of wildfires: A new dataset and machine learning approach”, Fire Safety Journal, 104: 130-146, (2019). DOI: https://doi.org/10.1016/j.firesaf.2019.01.006
  22. Ghorbanzadeh, O., Valizadeh Kamran, K., Blaschke, T., Aryal, J., Naboureh, A., Einali, J., and Bian, J., “Spatial prediction of wildfire susceptibility using field survey GPS data and machine learning approaches”, Fire, 2(3): 43, (2019). DOI: https://doi.org/10.3390/fire2030043
  23. Gholamnia, K., Gudiyangada Nachappa, T., Ghorbanzadeh, O., and Blaschke, T., “Comparisons of diverse machine learning approaches for wildfire susceptibility mapping”, Symmetry, 12(4): 604, (2020). DOI: https://doi.org/10.3390/sym12040604
  24. Sari, F., “Forest fire susceptibility mapping via multi-criteria decision analysis techniques for Mugla, Turkey: A comparative analysis of VIKOR and TOPSIS”, Forest Ecology and Management, 480, 118644, (2021). DOI: https://doi.org/10.1016/j.foreco.2020.118644
  25. Tezcan, B., and Eren, T., “Orman yangınına sebep olan kriterlerin bulanık ortamda değerlendirilmesi”, Politeknik Dergisi, 27(2): 545-558, (2023). DOI: 10.2339/politeknik.1138806
  26. Gulum, P., Ayyildiz, E., and Gumus, A. T., “A two level interval valued neutrosophic AHP integrated TOPSIS methodology for post-earthquake fire risk assessment: An application for Istanbul”, International Journal of Disaster Risk Reduction, 61, 102330, (2021). DOI: https://doi.org/10.1016/j.ijdrr.2021.102330
  27. Gündoğdu, F., “Generalization of intuitionistic, pythagorean and neutrosophıc fuzzy sets: spherical fuzzy sets and decision making”, Thesis (PhD), İstanbul Technical University Graduate School of Science, (2019).
  28. Gigović, L., Jakovljević, G., Sekulović, D., and Regodić, M., “GIS multi-criteria analysis for identifying and mapping forest fire hazard: Nevesinje, Bosnia and Herzegovina”, Tehnički Vjesnik, 25(3): 891-897, (2018). DOI: https://doi.org/10.17559/TV-20151230211722
  29. Abedi Gheshlaghi, H., Feizizadeh, B., and Blaschke, T., “GIS-based forest fire risk mapping using the analytical network process and fuzzy logic”, Journal of Environmental Planning and Management, 63(3): 481-499, (2020). DOI: https://doi.org/10.1080/09640568.2019.1594726
  30. Asori, M., Emmanuel, D., and Dumedah, G., “Wildfire hazard and risk modelling in the northern regions of Ghana using GIS-based multi-criteria decision making analysis”, Journal of Environment and Earth Science, 10(11), (2020). DOI: 10.7176/JEES/10-11-02
  31. Çoban, H., and Erdin, C., “Forest fire risk assessment using GIS and AHP integration in Bucak forest enterprise, Turkey”, Applied Ecology and Environmental Research, 18(1): 1567–1583 (2020). DOI: 10.15666/aeer/1801_15671583
  32. Van Hoang, T., Chou, T. Y., Fang, Y. M., Nguyen, N. T., Nguyen, Q. H., Xuan Canh, P., and Meadows, M. E., “Mapping forest fire risk and development of early warning system for NW Vietnam using AHP and MCA/GIS methods”, Applied Sciences, 10(12), 4348, (2020). DOI: https://doi.org/10.3390/app10124348
  33. Lamat, R., Kumar, M., Kundu, A., and Lal, D., “Forest fire risk mapping using analytical hierarchy process (AHP) and earth observation datasets: A case study in the mountainous terrain of Northeast India”, SN Applied Sciences, 3(4): 425, (2021). DOI: https://doi.org/10.1007/s42452-021-04391-0
  34. Nikhil, S., Danumah, J. H., Saha, S., Prasad, M. K., Rajaneesh, A., Mammen, P. C., and Kuriakose, S. L., “Application of GIS and AHP method in forest fire risk zone mapping: A study of the Parambikulam Tiger Reserve, Kerala, India”, Journal of Geovisualization and Spatial Analysis, 5(1): 14, (2021). DOI: https://doi.org/10.1007/s41651-021-00082-x
  35. Nuthammachot, N., and Stratoulias, D., “Multi-criteria decision analysis for forest fire risk assessment by coupling AHP and GIS: Method and case study”, Environment, Development and Sustainability, 23(12): 17443-17458, (2021). DOI: https://doi.org/10.1007/s10668-021-01394-0
  36. Abdo, H. G., Almohamad, H., Al Dughairi, A. A., and Al-Mutiry, M., “GIS-based frequency ratio and analytic hierarchy process for forest fire susceptibility mapping in the western region of Syria”, Sustainability, 14(8), 4668, (2022). DOI: https://doi.org/10.3390/su14084668
  37. Ghanbari Motlagh, M., Abbasnezhad Alchin, A., and Daghestani, M., “Detection of high fire risk areas in Zagros Oak forests using geospatial methods with GIS techniques”, Arabian Journal of Geosciences, 15(9): 835, (2022). DOI: https://doi.org/10.1007/s12517-022-10096-4
  38. Tezcan B., Pınarbaşı M., Alakaş H. M., and Eren T., “Orman Yangını Risk Değerlendirmesine Bulanık Bir Yaklaşım: Ege Bölgesi Örneği,” in 41. Yöneylem Araştırması ve Endüstri Mühendisliği (YA/EM) Ulusal Kongresi, 26-28 Ekim 2022, Denizli, Türkiye., (2022).
  39. Sivrikaya, F., and Küçük, Ö., “Modeling forest fire risk based on GIS-based analytical hierarchy process and statistical analysis in Mediterranean region”, Ecological Informatics, 68, 101537, (2022). DOI: https://doi.org/10.1016/j.ecoinf.2021.101537
  40. Sinha, A., Nikhil, S., Ajin, R. S., Danumah, J. H., Saha, S., Costache, R., and Abioui, M., “Wildfire risk zone mapping in contrasting climatic conditions: an approach employing AHP and F-AHP models”, Fire, 6(2): 44, (2023). DOI: https://doi.org/10.3390/fire6020044
  41. Ersoy, İ., Ünsal, E., and Gürsoy, Ö., “A multi-criteria forest fire danger assessment system on GIS using literature-based model and analytical hierarchy process model for Mediterranean Coast of Manavgat, Türkiye”, Sustainability, 17(5), 1971, (2025). DOI: https://doi.org/10.3390/su17051971
  42. Malik, F. A., Mushtaq, F., Farooq, M., Guite, L. S., Kanga, S., Meraj, G., and Kumar, P., “Assessing forest fire vulnerability with fuzzy-AHP: insights from Poonch forest division, Jammu and Kashmir”, Discover Forests, 1(1): 4, (2025). https://doi.org/10.1007/s44415-025-00004-5
  43. M. Riga, “An integrated approach to forest fire risk mapping in the Mediterranean Region-Evros, Greece,” Student Thesis Series INES, (2025).
  44. Uçar, Z., Güney, C. O., Akay, A. E., Bilici, E., and Erkan, N., “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, (2025). DOI: https://doi.org/10.1080/10807039.2025.2451146
  45. Tezcan, B., and Eren T., “Forest fire management and fire suppression strategies: a systematic literature review,” Natural Hazards, (2025). DOI:10.1007/s11069-025-07227-x.
  46. Saaty T. L., “The analytic hierarchy process (AHP)”, Journal of the Operational Research Society, 41(11): 1073-1076, (1980).
  47. Thor, J., Ding, S. H., and Kamaruddin, S., “Comparison of multi criteria decision making methods from the maintenance alternative selection perspective”, The International Journal of Engineering and Science, 2(6): 27-34, (2013).
  48. Radwan N. M., Senousy M. B., and Alaa El Din M. R., “Neutrosophic AHP multi criteria decision making method applied on the selection of learning management system”, Infinite Study, (2016).
  49. Bolturk, E., and Kahraman, C., “Interval-valued neutrosophic AHP with possibility degree method”, International Journal of the Analytic Hierarchy Process, 10(3), (2018). DOI: https://doi.org/10.13033/ijahp.v10i3.545
  50. Abdel-Basset, M., Mohamed, M., Hussien, A. N., and Sangaiah, A. K., “A novel group decision-making model based on triangular neutrosophic numbers”, Soft Computing, 22(20): 6629-6643, (2018). DOI: https://doi.org/10.1007/s00500-017-2758-5
  51. Kutlu Gündoğdu, F., and Kahraman, C., “Spherical fuzzy sets and spherical fuzzy TOPSIS method”, Journal of Intelligent and Fuzzy Systems, 36(1): 337-352, (2019). DOI: https://doi.org/10.3233/JIFS-181401
  52. Tezcan, B., and Eren, T., “Integer programming based on spherical fuzzy AHP and spherical fuzzy TOPSIS for wildfire equipment planning”, Environment Systems and Decisions, 45(3): 38, (2025). DOI: https://doi.org/10.1007/s10669-025-10029-y
  53. Tezcan, B., and Eren, T., “Orman yangınlarında iş sağlığı ve güvenliği uygulamalarının değerlendirilebilmesi için AHP ve ANP yöntemleri ile ölçütlerin belirlenmesi: Türkiye Örneği”, Ağaç ve Orman, 5(2): 98-105, (2024). DOI: 10.59751/agacorman.1497688
  54. Tezcan, B., and Eren, T., “Forest fire risk assessment in Balikesir using Pythagorean fuzzy AHP and Pythagorean fuzzy TOPSIS”, Politeknik Dergisi, (Advanced Online Publication), 1-1, (2025). DOI: 10.2339/politeknik.1648306