Abedi, R. (2022). Application of multi-criteria decision making models to forest fire management. International Journal of Geoheritage and Parks, 10(1), 84-96. https://doi.org/10.1016/j.ijgeop.2022.02.005
Achu, A. L., Thomas, J., Aju, C. D., Gopinath, G., Kumar, S., & Reghunath, R. (2021). Machine-learning modelling of fire susceptibility in a forest-agriculture mosaic landscape of southern India. Ecological Informatics, 64, 101348. https://doi.org/10.1016/j.ecoinf.2021.101348
Adab, H., Kanniah, K. D., & Solaimani, K. (2013). Modeling forest fire risk in the northeast of Iran using remote sensing and GIS techniques. Natural Hazards, 65(3), 1723-1743. https://doi.org/10.1007/s11069-012-0450-8
Alimahmoodi Sarab, S., Feghhi, J., & Jabarian Amiri, B. (2012). Predicting the occurrence of natural fires in forests and ranges using Artificial Neural Networks (Case study: Zagros region, Izeh township). Iranian Journal of Applied Ecology, 1(2), 75-86. [In Persian] https://dor.isc.ac/dor/20.1001.1.24763128.1391.1.2.7.1
Allouche, O., Tsoar, A., & Kadmon, R. (2006). Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). Journal of Applied Ecology, 43(6), 1223-1232.
Azizianpour, S., Mirzaei, J., Omidipour, R., & Jafarian, N. (2025). Machine learning-based forest fire susceptibility prediction in semiarid oak forests of western Iran. ECOPERSIA 13(1), 49-67. https://doi.org/10.22034/ECOPERSIA.13.1.49
Bahadori, N., Razavi-Termeh, S. V., Sadeghi-Niaraki, A., Al-Kindi, K. M., Abuhmed, T., Nazeri, B., & Choi, S. M. (2023). Wildfire susceptibility mapping using deep learning algorithms in two satellite imagery dataset. Forests, 14(7), 1325. https://doi.org/10.3390/f14071325
Bountzouklis, C., Fox, D. M., & Di Bernardino, E. (2022). Environmental factors affecting wildfire-burned areas in southeastern France, 1970–2019. Natural Hazards and Earth System Sciences, 22(4), 1181-1200. https://doi.org/10.5194/nhess-22-1181-2022
Bowman, D. M. J. S., Moreira-Muñoz, A., Kolden, C. A., Chávez, R. O., Muñoz, A. A., Salinas, F., González-Reyes, Á., … & Johnston, F. H. (2019). Human–environmental drivers and impacts of the globally extreme 2017 Chilean fires. Ambio, 48(4), 350-362. https://doi.org/10.1007/s13280-018-1084-1
Chouai, A., Laugier, S., & Richon, D. (2002). Modeling of thermodynamic properties using neural networks: Application to refrigerants. Fluid Phase Equilibria, 199(1), 53-62. https://doi.org/10.1016/S0378-3812(01)00801-9
Chuvieco, E., & Congalton, R. G. (1989). Application of remote sensing and geographic information systems to forest fire hazard mapping. Remote Sensing of Environment, 29(2), 147-159. https://doi.org/10.1016/0034-4257(89)90023-0
Denham, M. M., Waidelich, S., & Laneri, K. (2022). Visualization and modeling of forest fire propagation in Patagonia. Environmental Modelling & Software, 158, 105526. https://doi.org/10.1016/j.envsoft.2022.105526
Dimopoulou, M., & Giannikos, I. (2004). Towards an integrated framework for forest fire control. European Journal of Operational Research, 152(2), 476-486. https://doi.org/10.1016/S0377-2217(03)00038-9
Endreny, T. A., & Wood, E. F. (2003). Maximizing spatial congruence of observed and DEM-delineated overland flow networks. International Journal of Geographical Information Science, 17(7), 699-713. https://doi.org/10.1080/1365881031000135483
Eskandari, S., Pourghasemi, H. R., & Tiefenbacher, J. P. (2020). Relations of land cover, topography, and climate to fire occurrence in natural regions of Iran: Applying new data mining techniques for modeling and mapping fire danger. Forest Ecology and Management, 473, 118338. https://doi.org/10.1016/j.foreco.2020.118338
Farahi Ashtiani, E., Daryaei, M. G., & Mohamadi Samani, K. (2013). Amin Amlashi, M., Review of fire sensitive areas with emphasis on drought impact with the joint use of PDSI, AHP and GIS (Case study: Forest Saravan, Guilan province). Forest and Range Protection Research 10(2), 83-110. [In Persian] https://doi.org/10.22092/ijfrpr.2012.11141
Gholami, H., Mohamadifar, A., Rahimi, S., Kaskaoutis, D. G., & Collins, A. L. (2021). Predicting land susceptibility to atmospheric dust emissions in central Iran by combining integrated data mining and a regional climate model. Atmospheric Pollution Research, 12(4), 172-187. https://doi.org/10.1016/j.apr.2021.03.005
Ghorbanzadeh, O., & Blaschke, T. (2018). Wildfire susceptibility evaluation by integrating an analytical network process approach into GIS-based analyses. International Journal on Advanced Science. Engineering and Information Technology 6, 48-53.
Guisan, A., Edwards, T. C., & Hastie, T. (2002). Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecological Modelling, 157(2), 89-100. https://doi.org/10.1016/S0304-3800(02)00204-1
Hamedi, N., Esmaeily, A., & Faramarzi, H. (2020). Analysis of the potential fire hazard scenarios using GIS and RS: A case study of Lordegan forests. Emergency Management 9(1), 17-27. https://dor.isc.ac/dor/20.1001.1.23453915.1399.9.1.2.5
Hastie, T., & Tibshirani, R. (1987). Non-parametric logistic and proportional odds regression. Journal of the Royal Statistical Society. Series C (Applied Statistics), 36(3), 260-276. https://doi.org/10.2307/2347785
He, M. Z., Zheng, J. G., Li, X. R., & Qian, Y. L. (2007). Environmental factors affecting vegetation composition in the Alxa Plateau, China. Journal of Arid Environments, 69(3), 473-489. https://doi.org/10.1016/j.jaridenv.2006.10.005
Hong, H., Tsangaratos, P., Ilia, I., Liu, J., Zhu, A. X., & Xu, C. (2018). Applying genetic algorithms to set the optimal combination of forest fire related variables and model forest fire susceptibility based on data mining models. The case of Dayu County, China. Science of The Total Environment, 630, 1044-1056. https://doi.org/10.1016/j.scitotenv.2018.02.278
Janizadeh, S., Bateni, S. M., Jun, C., Im, J., Pai, H. T., Band, S. S., & Mosavi, A. (2023). Combination four different ensemble algorithms with the generalized linear model (GLM) for predicting forest fire susceptibility. Geomatics, Natural Hazards and Risk, 14(1), 2206512. https://doi.org/10.1080/19475705.2023.2206512
Lasslop, G., & Kloster, S. (2017). Human impact on wildfires varies between regions and with vegetation productivity. Environmental Research Letters, 12(11), 115011. https://doi.org/10.1088/1748-9326/aa8c82
Mambile, C., Kaijage, S., & Leo, J. (2025). Deep learning models for enhanced forest-fire prediction at Mount Kilimanjaro, Tanzania: Integrating satellite images, weather data and human activities data. Natural Hazards Research, 5(2), 335-347. https://doi.org/10.1016/j.nhres.2024.12.001
Mishra, B., Panthi, S., Poudel, S., & Ghimire, B. R. (2023). Forest fire pattern and vulnerability mapping using deep learning in Nepal. Fire Ecology 19(1), 1-15. https://doi.org/10.1186/s42408-022-00162-3
Mohajane, M., Costache, R., Karimi, F., Bao Pham, Q., Essahlaoui, A., Nguyen, H., Laneve, G., & Oudija, F. (2021). Application of remote sensing and machine learning algorithms for forest fire mapping in a Mediterranean area. Ecological Indicators, 129, 107869. https://doi.org/10.1016/j.ecolind.2021.107869
Moradi, S., & Ahmadi Sani, N. (2025). Wildfire risk assessment and spatial zoning in forests and rangelands using GIS-based multi-criteria decision-making techniques in Central Zagros. Iranian Journal of Forest, 16(4), 531-554. [In Persian] https://doi.org/10.22034/ijf.2024.465288.1994
Myerson, R. B. (2013). Game Theory: Analysis of Conflict. Harvard University Press.
Najafi, A., Irannezhad, M. H., Sotoudeh, A., Mokhtari, M. H., & Kiani, B. (2015). Modeling and risk mapping of forest fires using remote sensing and GIS (Case study: Baghe-Shadi protected area, Yazd province). Iranian Journal of Applied Ecology 4(14), 13-26. [In Persian] https://doi.org/10.18869/acadpub.ijae.4.14.13
Pham, B. T., Jaafari, A., Avand, M., Al-Ansari, N., Dinh Du, T., Yen, H. P., Phong, T. V., … & Tuyen, T. T. (2020). Performance evaluation of machine learning methods for forest fire modeling and prediction. Symmetry, 12(6), 1022. https://doi.org/10.3390/sym12061022
Phillips, S. J., Anderson, R. P., & Schapire, R. E. (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190(3), 231-259. https://doi.org/10.1016/j.ecolmodel.2005.03.026
Polat, S., Ghasemi Aghbash, F., & Mahdavi, A. (2020). Forest fire hazard zone mapping in Ilam County forests. Forest Research and Development, 6(1), 135-152. https://doi.org/10.30466/jfrd.2020.120830
Pragya Kumar, M., Tiwari, A., Majid, S. I., Bhadwal, S., Sahu, N., Verma, N. K., Tripathi, D. K., & Avtar, R. (2023). Integrated spatial analysis of forest fire susceptibility in the Indian western Himalayas (IWH) using remote sensing and GIS-based fuzzy AHP approach. Remote Sensing, 15(19), 4701. https://doi.org/10.3390/rs15194701
Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1974). Monitoring vegetation system in the Great Plains with ERTS. Proceedings of the Third Earth Resources Technology Satellite-1 Symposium, Greenbelt, USA; NASA SP-351.
Sandholt, I., Rasmussen, K., & Andersen, J. (2002). A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status. Remote Sensing of Environment, 79(2), 213-224. https://doi.org/10.1016/S0034-4257(01)00274-7
Sarkar, M. S., Majhi, B. K., Pathak, B., Biswas, T., Mahapatra, S., Kumar, D., Bhatt, I. D., … & Nautiyal, S. (2024). Ensembling machine learning models to identify forest fire-susceptible zones in Northeast India. Ecological Informatics, 81, 102598. https://doi.org/10.1016/j.ecoinf.2024.102598
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. https://doi.org/10.1016/j.ecoinf.2021.101537
Tessler, N., Wittenberg, L., & Greenbaum, N. (2016). Vegetation cover and species richness after recurrent forest fires in the Eastern Mediterranean ecosystem of Mount Carmel, Israel. Science of The Total Environment, 572, 1395-1402. https://doi.org/10.1016/j.scitotenv.2016.02.113
Tuyen, T. T., Jaafari, A., Yen, H. P. H., Nguyen-Thoi, T., Phong, T. V., Nguyen, H. D., Van Le, H., … & Pham, B. T. (2021). Mapping forest fire susceptibility using spatially explicit ensemble models based on the locally weighted learning algorithm. Ecological Informatics, 63, 101292. https://doi.org/10.1016/j.ecoinf.2021.101292
Wasserman, T. N., & Mueller, S. E. (2023). Climate influences on future fire severity: a synthesis of climate-fire interactions and impacts on fire regimes, high-severity fire, and forests in the western United States. Fire Ecology, 19(1), 43. https://doi.org/10.1186/s42408-023-00200-8
Weise, D. R., & Biging, G. S. (1996). Effects of wind velocity and slope on flame properties Canadian Journal of Forest Research, 26(10), 1849-1858. https://doi.org/10.1139/x26-210
Zare Chahouki, M. A., Karami, P., & Piri Sahragard, H. (2022). Ensemble modeling approach to predict the potential distribution of Artemisia sieberi in desert rangelands of Yazd province, central Iran. Journal of Rangeland Science, 12(4), 326-340. https://doi.org/10.30495/rs.2022.685569
Zhang, G., Wang, M., & Liu, K. (2019). Forest fire susceptibility modeling using a convolutional neural network for Yunnan province of China. International Journal of Disaster Risk Science, 10(3), 386-403. https://doi.org/10.1007/s13753-019-00233-1
Zhou, Z., Qiu, C., & Zhang, Y. (2023). A comparative analysis of linear regression, neural networks and random forest regression for predicting air ozone employing soft sensor models. Scientific Reports, 13(1), 22420. https://doi.org/10.1038/s41598-023-49899-0
