Spherical Fuzzy AHP and Spherical Fuzzy TOPSIS-Based Goal Programming for Forest Fire Resource Allocation of Kastamonu Province
Yıl 2026,
Cilt: 26 Sayı: 1, 75 - 94, 27.03.2026
Burcu Tezcan
,
Tamer Eren
Öz
Aim of study: The study aims to assess the risk of wildfires in Kastamonu to develop a resource allocation plan through an optimization approach for strategic resource allocation in firefighting.
Area of study: The total forest area in Kastamonu was 876314 hectares. The region around the Ilgaz Mountains, which are in the inland areas away from the coastline, was found to be vulnerable to fire risk.
Material and method: Four major criteria and twelve sub-criteria that affect fire occurrence were assessed using SF-AHP. The weights were shortlisted using SF-TOPSIS. After determining the fire-vulnerable regions, resource allocation was done using goal programming.
Main results: Proximity to power lines was found to be the most important factor affecting the forest fire risk, with a weight of 13.5%, followed by humidity levels with a weight of 12.6%. Human factors and meteorological factors played a crucial role in determining the fire risk. The results revealed that the highest fire risk occurred in the Taşköprü district.
Research highlights: This study contributed to the literature with a new approach in which fuzzy-based risk analysis and goal programming models were used for the first time.
Kaynakça
-
Abdo, H. G., Almohamad, H., Al Dughairi, A. A. & Al-Mutiry, M. (2022). GIS-based frequency ratio and analytic hierarchy process for forest fire susceptibility mapping in the western region of Syria. Sustainability, 14(8), 4668.
-
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.
-
Alkayış, M. H., Karslıoğlu, A. & Onur, M. İ. (2020). Muğla ili Menteşe yöresi orman yangını risk potansiyeli haritasının coğrafi bilgi sistemleri ile belirlenmesi. Geomatik, 7(1), 10-16.
-
Asori, M., Emmanuel, D. & Dumedah, G. (2020). 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).
-
Bashiri, M., Nikzad, E., Eberhard, A., Hearne, J. & Oliveira, F. (2021). A two stage stochastic programming for asset protection routing and a solution algorithm based on the Progressive Hedging algorithm. Omega, 104, 102480.
-
Brown, G. G., Koyak, R. A., Salmerón, J. & Scholz, Z. (2021). Optimizing prepositioning of equipment and personnel for Los Angeles County Fire Department to fight wildland fires. INFORMS Journal on Applied Analytics, 51(6), 435-449.
-
Coban, H. & Erdin, C. (2020). Forest fire risk assessment using GIS and AHP integration in Bucak forest enterprise, Turkey. Applied Ecology and Environmental Research, 18(1).
-
Dağdeviren, M. & Eren, T. (2001). Tedarikçi firma seçiminde analitik hiyerarşi prosesi ve 0-1 hedef programlama yöntemlerinin kullanılması. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 16(1), 41-52.
-
Ergün, D. (2006). Hedef programlama ile üretim planlaması. Yüksek Lisans Tezi, Hacettepe Üniversitesi, Fen Bilimleri Enstitüsü, Ankara, Türkiye.
-
Ersoy, İ., Ünsal, E. & Gürsoy, Ö. (2025). 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.
-
Forootani, A., Tipaldi, M., Ghaniee Zarch, M., Liuzza, D. & Glielmo, L. (2021). Modelling and solving resource allocation problems via a dynamic programming approach. International Journal of Control, 94(6), 1544-1555.
-
Ghanbari Motlagh, M., Abbasnezhad Alchin, A. & Daghestani, M. (2022). Detection of high fire risk areas in Zagros Oak forests using geospatial methods with GIS techniques. Arabian Journal of Geosciences, 15(9), 835.
-
Gigović, L., Jakovljević, G., Sekulović, D. & Regodić, M. (2018). GIS multi-criteria analysis for identifying and mapping forest fire hazard: Nevesinje, Bosnia and Herzegovina. Tehnički vjesnik, 25(3), 891-897.
-
Granda, B., León, J., Vitoriano, B. & Hearne, J. (2023). Decision Support Models and Methodologies for Fire Suppression. Fire, 6(2), 37.
-
Gündoğdu, F. (2019). Generalization of intuitionistic, pythagorean and neutrosophıc fuzzy sets: spherical fuzzy sets and decision making. Ph. D. Thesis, İstanbul Technical University Graduate School of Science.
-
Gür, Ş., Hamurcu, M. & Eren, T. (2017). Ankara’da monoray projelerinin analitik hiyerarşi prosesi ve 0-1 hedef programlama yöntemleri ile seçimi. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 23(4), 437-443.
-
Kavzoğlu, T. (2016). Orman Yangınları. Orman Genel Müdürlüğü, Ankara, 466.
-
Lamat, R., Kumar, M., Kundu, A. & Lal, D. (2021). 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.
-
Li, D., Cova, T. J. & Dennison, P. E. (2019). Setting wildfire evacuation triggers by coupling fire and traffic simulation models: A spatiotemporal GIS approach. Fire technology, 55, 617-642.
-
Malik, F. A., Mushtaq, F., Farooq, M., Guite, L. T. S., Kanga, S., et al. (2025). Assessing forest fire vulnerability with fuzzy-AHP: insights from Poonch forest division, Jammu and Kashmir. Discover Forests, 1(1), 4.
-
Matos, M. A., Rocha, A. M. A. C., Costa, L. A. & Alvelos, F. (2023). A genetic algorithm to optimize the dispatch of firefighting resources. Numerical Computations: Theory and Algorithms NUMTA 2023, 149.
-
Nami, M. H., Jaafari, A., Fallah, M. & Nabiuni, S. (2018). Spatial prediction of wildfire probability in the Hyrcanian ecoregion using evidential belief function model and GIS. International Journal of Environmental Science and Technology, 15, 373-384.
-
Nikhil, S., Danumah, J. H., Saha, S., Prasad, M. K., Rajaneesh, A., Mammen, P. C., Ajin, R. S. & Kuriakose, S. L. (2021). 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.
-
Novo, A., Fariñas-Álvarez, N., Martínez-Sánchez, J., González-Jorge, H., Fernández-Alonso, J. M. & Lorenzo, H. (2020). Mapping forest fire risk-a case study in Galicia (Spain). Remote Sensing, 12(22), 3705.
-
Nuthammachot, N. & Stratoulias, D. (2021). 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.
-
Özder, E. H. (2015). Tedarikçi seçiminde analitik ağ süreci ve hedef programlama tekniklerinin entegrasyonu: Örnek olay çalışması. Yüksek Lisans Tezi, Kırıkkale Üniversitesi, Fen Bilimleri Enstitüsü, Kırıkkale, Türkiye.
-
Palacios-Meneses, D., Carrasco, J., Dávila, S., Martínez, M., Mahaluf, R. & Weintraub, A. (2023). Comparison of metaheuristics for the firebreak placement problem: A simulation-based optimization approach. arXiv preprint arXiv:2311.17393.
-
Riga, M. (2025). An integrated approach to forest fire risk mapping in the Mediterranean Region-Evros, Greece. Student thesis series INES.
-
Rothermel R. C. (1983). How to predict the spread and intensity of forest and Range fires. Books, 143.
-
Sari, F. (2021). 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.
-
Sinha, A., Nikhil, S., Ajin, R. S., Danumah, J. H., Saha, S., et al. (2023). Wildfire risk zone mapping in contrasting climatic conditions: An approach employing AHP and F-AHP models. Fire, 6(2), 44.
-
Sivrikaya, F. & Küçük, Ö. (2022). Modeling forest fire risk based on GIS-based analytical hierarchy process and statistical analysis in Mediterranean region. Ecological Informatics, 68, 101537.
-
Suarez, D., Gomez, C., Medaglia, A. L., Akhavan-Tabatabaei, R. & Grajales, S. (2024). Integrated decision support for disaster risk management: Aiding preparedness and response decisions in wildfire management. Information Systems Research.
-
Tapia, T., Lorca, Á., Olivares, D. & Negrete-Pincetic, M. (2021). A robust decision-support method based on optimization and simulation for wildfire resilience in highly renewable power systems. European Journal of Operational Research, 294(2), 723-733.
-
Tezcan, B. & Eren, T. (2025). Forest fire management and fire suppression strategies: A systematic literature review. Natural Hazards.
-
Tezcan, B., Alakaş, H. M., Özcan, E. & Eren, T. (2021). Afet sonrası geçici depo yeri seçimi ve çok araçlı araç rotalama uygulaması: Kırıkkale ilinde bir uygulama. Politeknik Dergisi, 26(1), 13-27.
-
Tezcan, B. & Eren, T. (2022). Orman Yangınlarına Etki Eden Faktörlerin Önceliklendirilmesi. 3rd International Disaster Management Congress.
-
Tezcan, B. & Eren, T. (2023). Orman Yangınına Sebep Olan Kriterlerin Bulanık Ortamda Değerlendirilmesi. Politeknik Dergisi, 27(2), 545-558.
-
Tezcan, B. & Eren, T. (2025a). Forest fire resource planning with integer programming: An application in Turkey. Forest Science and Technology, 1-8.
-
Tezcan, B. & Eren, T. (2025b). Forest Fire risk assessment in Balikesir using pythagorean fuzzy AHP and pythagorean fuzzy TOPSIS. Politeknik Dergisi, 1.
-
Tezcan, B. & Eren, T. (2025c). Optimizing firefighting equipment allocation in Balıkesir using 0-1 integer programming. Turkish Journal of Forest Science, 9(1), 203-216.
-
Tezcan, B., Pınarbaşı, M., Alakaş, H. M. & Eren, T. (2022). Orman Yangını Risk Değerlendirmesine Bulanık Bir Yaklaşım: Ege Bölgesi Örneği. 41. Yöneylem Araştırması ve Endüstri Mühendisliği (YA/EM) Ulusal Kongresi, 26-28 Ekim 2022, Denizli, Türkiye.
-
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, 1-26.
-
URL-1. (2025). Ministry of Agriculture and Forestry, General Directorate of Forestry. https://www.ogm.gov.tr/tr/e-kutuphane/resmi-istatistikler, Erişim Tarihi: 15.01.2025.
-
Van der Merwe, M. (2015). An optimisation approach for assigning resources to defensive tasks during wildfires. RMIT University.
-
Van Hoang, T., Chou, T. Y., Fang, Y. M., Nguyen, N. T., Nguyen, Q. H., et al. (2020). Mapping forest fire risk and development of early warning system for NW Vietnam using AHP and MCA/GIS methods. Applied Sciences, 10(12), 4348.
-
Wu, P., Cheng, J. & Feng, C. (2019). Resource‐constrained emergency scheduling for forest fires with priority areas: An efficient ınteger‐programming approach. IEEJ Transactions on Electrical and Electronic Engineering, 14(2), 261-270.
-
Zhang, H., Zhao, X., Fang, X. & Chen, B. (2023). Proactive resource request for disaster response: A deep learning-based optimization model. Information Systems Research.
Kastamonu İlinin Orman Yangını Kaynak Tahsisi için Spherical Bulanık AHP ve Spherical Bulanık TOPSIS Tabanlı Hedef Programlama
Yıl 2026,
Cilt: 26 Sayı: 1, 75 - 94, 27.03.2026
Burcu Tezcan
,
Tamer Eren
Öz
Çalışmanın amacı: Bu çalışma Kastamonu’nun orman yangını riski yüksek olan ilçesini belirleyerek kaynak tahsis planlaması yapılması amaçlanmıştır. Model orman yangınlarına en uygun ekipmanların atanmasını hedeflemektedir.
Çalışma alanı: Kastamonu 876314 hektar orman alanına sahiptir. Kıyıdan uzak iç kesimlerde bulunan Ilgaz Dağları’nın çevresi yangın riski açısından hassastır.
Materyal ve yöntem: 4 ana kriter ve alt kriterlerin belirlenmesi için SF-AHP yöntemi uygulanmış, ağırlıkların hesaplanması için ise SF-TOPSIS yöntemi tercih edilmiştir. Yangın riskine etki eden bölgelerin belirlenmesinden sonra, hedef programlama yöntemi ile kaynak tahsisi planlaması gerçekleştirilmiştir.
Temel sonuçlar: Orman yangını riski açısından en kritik % 13.5 ağırlıkla elektrik hatlarına yakınlık olup bu kriteri % 12.6 oranında nem seviyesi takip etmektedir. İnsan kaynaklı etkenlerin ve meteorolojik koşulların yangın riskini belirlemede önemlidir. Yapılan analizler sonucunda Taşköprü ilçesi en risklidir.
Araştırma vurguları: Literatürde ilk kez bulanık tabanlı risk analizi ve hedef programlama modeli birleştirilerek yeni bir yaklaşım sunulmuştur.
Kaynakça
-
Abdo, H. G., Almohamad, H., Al Dughairi, A. A. & Al-Mutiry, M. (2022). GIS-based frequency ratio and analytic hierarchy process for forest fire susceptibility mapping in the western region of Syria. Sustainability, 14(8), 4668.
-
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.
-
Alkayış, M. H., Karslıoğlu, A. & Onur, M. İ. (2020). Muğla ili Menteşe yöresi orman yangını risk potansiyeli haritasının coğrafi bilgi sistemleri ile belirlenmesi. Geomatik, 7(1), 10-16.
-
Asori, M., Emmanuel, D. & Dumedah, G. (2020). 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).
-
Bashiri, M., Nikzad, E., Eberhard, A., Hearne, J. & Oliveira, F. (2021). A two stage stochastic programming for asset protection routing and a solution algorithm based on the Progressive Hedging algorithm. Omega, 104, 102480.
-
Brown, G. G., Koyak, R. A., Salmerón, J. & Scholz, Z. (2021). Optimizing prepositioning of equipment and personnel for Los Angeles County Fire Department to fight wildland fires. INFORMS Journal on Applied Analytics, 51(6), 435-449.
-
Coban, H. & Erdin, C. (2020). Forest fire risk assessment using GIS and AHP integration in Bucak forest enterprise, Turkey. Applied Ecology and Environmental Research, 18(1).
-
Dağdeviren, M. & Eren, T. (2001). Tedarikçi firma seçiminde analitik hiyerarşi prosesi ve 0-1 hedef programlama yöntemlerinin kullanılması. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 16(1), 41-52.
-
Ergün, D. (2006). Hedef programlama ile üretim planlaması. Yüksek Lisans Tezi, Hacettepe Üniversitesi, Fen Bilimleri Enstitüsü, Ankara, Türkiye.
-
Ersoy, İ., Ünsal, E. & Gürsoy, Ö. (2025). 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.
-
Forootani, A., Tipaldi, M., Ghaniee Zarch, M., Liuzza, D. & Glielmo, L. (2021). Modelling and solving resource allocation problems via a dynamic programming approach. International Journal of Control, 94(6), 1544-1555.
-
Ghanbari Motlagh, M., Abbasnezhad Alchin, A. & Daghestani, M. (2022). Detection of high fire risk areas in Zagros Oak forests using geospatial methods with GIS techniques. Arabian Journal of Geosciences, 15(9), 835.
-
Gigović, L., Jakovljević, G., Sekulović, D. & Regodić, M. (2018). GIS multi-criteria analysis for identifying and mapping forest fire hazard: Nevesinje, Bosnia and Herzegovina. Tehnički vjesnik, 25(3), 891-897.
-
Granda, B., León, J., Vitoriano, B. & Hearne, J. (2023). Decision Support Models and Methodologies for Fire Suppression. Fire, 6(2), 37.
-
Gündoğdu, F. (2019). Generalization of intuitionistic, pythagorean and neutrosophıc fuzzy sets: spherical fuzzy sets and decision making. Ph. D. Thesis, İstanbul Technical University Graduate School of Science.
-
Gür, Ş., Hamurcu, M. & Eren, T. (2017). Ankara’da monoray projelerinin analitik hiyerarşi prosesi ve 0-1 hedef programlama yöntemleri ile seçimi. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 23(4), 437-443.
-
Kavzoğlu, T. (2016). Orman Yangınları. Orman Genel Müdürlüğü, Ankara, 466.
-
Lamat, R., Kumar, M., Kundu, A. & Lal, D. (2021). 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.
-
Li, D., Cova, T. J. & Dennison, P. E. (2019). Setting wildfire evacuation triggers by coupling fire and traffic simulation models: A spatiotemporal GIS approach. Fire technology, 55, 617-642.
-
Malik, F. A., Mushtaq, F., Farooq, M., Guite, L. T. S., Kanga, S., et al. (2025). Assessing forest fire vulnerability with fuzzy-AHP: insights from Poonch forest division, Jammu and Kashmir. Discover Forests, 1(1), 4.
-
Matos, M. A., Rocha, A. M. A. C., Costa, L. A. & Alvelos, F. (2023). A genetic algorithm to optimize the dispatch of firefighting resources. Numerical Computations: Theory and Algorithms NUMTA 2023, 149.
-
Nami, M. H., Jaafari, A., Fallah, M. & Nabiuni, S. (2018). Spatial prediction of wildfire probability in the Hyrcanian ecoregion using evidential belief function model and GIS. International Journal of Environmental Science and Technology, 15, 373-384.
-
Nikhil, S., Danumah, J. H., Saha, S., Prasad, M. K., Rajaneesh, A., Mammen, P. C., Ajin, R. S. & Kuriakose, S. L. (2021). 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.
-
Novo, A., Fariñas-Álvarez, N., Martínez-Sánchez, J., González-Jorge, H., Fernández-Alonso, J. M. & Lorenzo, H. (2020). Mapping forest fire risk-a case study in Galicia (Spain). Remote Sensing, 12(22), 3705.
-
Nuthammachot, N. & Stratoulias, D. (2021). 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.
-
Özder, E. H. (2015). Tedarikçi seçiminde analitik ağ süreci ve hedef programlama tekniklerinin entegrasyonu: Örnek olay çalışması. Yüksek Lisans Tezi, Kırıkkale Üniversitesi, Fen Bilimleri Enstitüsü, Kırıkkale, Türkiye.
-
Palacios-Meneses, D., Carrasco, J., Dávila, S., Martínez, M., Mahaluf, R. & Weintraub, A. (2023). Comparison of metaheuristics for the firebreak placement problem: A simulation-based optimization approach. arXiv preprint arXiv:2311.17393.
-
Riga, M. (2025). An integrated approach to forest fire risk mapping in the Mediterranean Region-Evros, Greece. Student thesis series INES.
-
Rothermel R. C. (1983). How to predict the spread and intensity of forest and Range fires. Books, 143.
-
Sari, F. (2021). 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.
-
Sinha, A., Nikhil, S., Ajin, R. S., Danumah, J. H., Saha, S., et al. (2023). Wildfire risk zone mapping in contrasting climatic conditions: An approach employing AHP and F-AHP models. Fire, 6(2), 44.
-
Sivrikaya, F. & Küçük, Ö. (2022). Modeling forest fire risk based on GIS-based analytical hierarchy process and statistical analysis in Mediterranean region. Ecological Informatics, 68, 101537.
-
Suarez, D., Gomez, C., Medaglia, A. L., Akhavan-Tabatabaei, R. & Grajales, S. (2024). Integrated decision support for disaster risk management: Aiding preparedness and response decisions in wildfire management. Information Systems Research.
-
Tapia, T., Lorca, Á., Olivares, D. & Negrete-Pincetic, M. (2021). A robust decision-support method based on optimization and simulation for wildfire resilience in highly renewable power systems. European Journal of Operational Research, 294(2), 723-733.
-
Tezcan, B. & Eren, T. (2025). Forest fire management and fire suppression strategies: A systematic literature review. Natural Hazards.
-
Tezcan, B., Alakaş, H. M., Özcan, E. & Eren, T. (2021). Afet sonrası geçici depo yeri seçimi ve çok araçlı araç rotalama uygulaması: Kırıkkale ilinde bir uygulama. Politeknik Dergisi, 26(1), 13-27.
-
Tezcan, B. & Eren, T. (2022). Orman Yangınlarına Etki Eden Faktörlerin Önceliklendirilmesi. 3rd International Disaster Management Congress.
-
Tezcan, B. & Eren, T. (2023). Orman Yangınına Sebep Olan Kriterlerin Bulanık Ortamda Değerlendirilmesi. Politeknik Dergisi, 27(2), 545-558.
-
Tezcan, B. & Eren, T. (2025a). Forest fire resource planning with integer programming: An application in Turkey. Forest Science and Technology, 1-8.
-
Tezcan, B. & Eren, T. (2025b). Forest Fire risk assessment in Balikesir using pythagorean fuzzy AHP and pythagorean fuzzy TOPSIS. Politeknik Dergisi, 1.
-
Tezcan, B. & Eren, T. (2025c). Optimizing firefighting equipment allocation in Balıkesir using 0-1 integer programming. Turkish Journal of Forest Science, 9(1), 203-216.
-
Tezcan, B., Pınarbaşı, M., Alakaş, H. M. & Eren, T. (2022). Orman Yangını Risk Değerlendirmesine Bulanık Bir Yaklaşım: Ege Bölgesi Örneği. 41. Yöneylem Araştırması ve Endüstri Mühendisliği (YA/EM) Ulusal Kongresi, 26-28 Ekim 2022, Denizli, Türkiye.
-
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, 1-26.
-
URL-1. (2025). Ministry of Agriculture and Forestry, General Directorate of Forestry. https://www.ogm.gov.tr/tr/e-kutuphane/resmi-istatistikler, Erişim Tarihi: 15.01.2025.
-
Van der Merwe, M. (2015). An optimisation approach for assigning resources to defensive tasks during wildfires. RMIT University.
-
Van Hoang, T., Chou, T. Y., Fang, Y. M., Nguyen, N. T., Nguyen, Q. H., et al. (2020). Mapping forest fire risk and development of early warning system for NW Vietnam using AHP and MCA/GIS methods. Applied Sciences, 10(12), 4348.
-
Wu, P., Cheng, J. & Feng, C. (2019). Resource‐constrained emergency scheduling for forest fires with priority areas: An efficient ınteger‐programming approach. IEEJ Transactions on Electrical and Electronic Engineering, 14(2), 261-270.
-
Zhang, H., Zhao, X., Fang, X. & Chen, B. (2023). Proactive resource request for disaster response: A deep learning-based optimization model. Information Systems Research.