Abdusalomov, A.B., Islam, B.M.S., Nasimov, R., Mukhiddinov, M., & Whangbo, T.K. (2023). An improved forest fire detection method based on the detectron2 model and a deep learning approach. Sensors, 23(3), 1512.
Abdusalomov, A.B., Islam, B.M.S., Nasimov, R., Mukhiddinov, M., & Whangbo, T.K. (2023). An improved forest fire detection method based on the detectron2 model and a deep learning approach. Sensors, 23(3), 1512.
Ahmadi, K., Alavi, S.J., Amiri, G.Z., Hosseini, S.M., Serra-Diaz, J.M., & Svenning, J.C. (2020). The potential impact of future climate on the distribution of European yew (Taxus baccata L.) in the Hyrcanian Forest region (Iran). International Journal of Biometeorology, 64, 1451-1462. (In persian)
Azizi, M., Khosravi, M., & Pourreza, M. (2022). Time Series model of fires forests and rangelands of Kermanshah province using MODIS data from 2002 to 2018. Iranian Journal of Forest and Range Protection Research, 19(2), 279-296. (In persian)
Breiman, L.J., HFriedman, R.A., Olshen, C.J.).1984). Classification and regression trees, P368.
Burgess, R. (2011). Development of a spatial, dynamic, fuzzy fire risk model for Chitwan District, Nepal. M.Sc. thesis, Faculty of Geo-Information Science and Earth Observation, University of Twente, 96p.
Chang, Y., Zhu, Z., Bu, R., Chen, H., Feng, Y., Li, Y., Hu, Y., & Wang, Z. (2013). Predicting fire occurrence patterns with logistic regression in Heilongjiang Province, China. Landscape Ecology, 28(10), 1989-2004. https://doi.org/10.1007/s10980-013-9935-4
Chavan, M.E., Das, K.K., & Suryawanshi, R.S. (2012). Forest fire risk zonation using remote sensing and GIS in Huynial watershed, Tehri Garhwal district, UA. International Journal of Basic and Applied Research, 2(7), 6-12.
Chen, W., Xie, X., Wang, J., Pradhan, B., Hong, H., Bui, D.T.,& Ma, J. (2017). A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility. Catena, 151, 147-160.
Dimopoulou, M., & Giannikos, I. (2004). Towards an integrated framework for forest fire control. European Journal of Operational Research, 152(2), 476-486.
Dong, X., Li-min, D., Guo-fan, Sh., Lei, T., Hui, W. (2005). Forest fire risk zone mapping from satellite images and GIS for Baihe forestry Bureau, Jilin, China. Journal of Forestry Research, 3(16), 169- 174
Eshaghi, M.A., & Shataeei, S. (2016). Preparation map of Forest Fire Risk Using SVM, RF & MLP Algorithms (Case Study: Golestan National Park, Northeastern Iran). Journal of Wood and Forest Science and Technology, 23(4), 1333-154. (In persian)
Eskandari, S. (2017). Methods of modeling and evaluation of fire occurrence risk in the forests of world and Iran. Human & Environment, 15(3), 91-110. (In persian)
Eskandari, S., & Eskandari, S. (2021). Fire of Iranian forests, consequences, opposition methods and solutions. Human and Environment, 19(1), 175-187. (In persian)
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
Natural Resources and Watershed Organization, (2007). Forest Management Plans for Zones 30 to 36. (In persian)
Hajimohammadi, H., Baaqideh, M., & Fallah Ghalehri, G.A. (2017). Investigating Atmospheric Structure at the Time of Wildfire Occurrence in Northern Iran. Geographical Planning of Space, 7(25), 187-206.
Himmy, O., & Rhinane, H. (2023). Landslide Susceptibility Mapping Using Machine Learning Algorithms Study Case AL Hoceima Region, Northern Morocco. The International Archives of the Photogrammetry. Remote Sensing and Spatial Information Sciences, 48, 153-158.
Ikhsan, A.N., Hadmoko, D.S., & Widayani, P. (2023). Spatial Modeling of Forest and Land Fire Susceptibility Using the Information Value Method in Kotawaringin Barat Regency, Indonesia. Fire, 6(4), 170. https://doi.org/10.3390/fire6040170
Jain, P., Coogan, S.C., Subramanian, S.G., Crowley, M., Taylor, S., & Flannigan, M.D. (2020). A review of machine learning applications in wildfire science and management. Environmental Reviews, 28(4), 478-505.
Jiao, Q., Fan, M., Tao, J., Wang, W., Liu, D., & Wang, P. (2023). Forest fire patterns and lightning-caused forest fire detection in Heilongjiang Province of China using satellite data. Fire, 6(4), 166.
Lymberopoulos, N., Papadopoulos, C., Stefanakis, E., Pantalos, N., & Lockwood, F. )1996). A GIS -based forest fire management information system. EARSel Journal–Advances in Remote Sensing, 4(1), 68-75.
Makhdoom, M., Darvish Safat, A., Jafarzadeh, H., & Makhdoom, A. (2003). Environmental Assessment and Planning with Geographic Information Systems "GIS". Tehran University Press, 304 p.
Marvi Mohajer, M.R. (2007). Sylviculture, University of Tehran Publication, 2709, 387. (In persian)
Najafi, A., Irannezhad, M.H., Sotoudeh, A., Mokhtari, M.H., Kiani, B. (2016). 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)
Omidi, M., Mafi Gholami, D., Mahmoodi, B., & Jafari, A. (2020). Spatial modeling the probability of wildfire occurrence using frequency ratio and weight- of-evidence models. Iranian Journal of Forest and Range Protection Research, 17(2), 125-144. (In persian)
Omidi, M., Mafi Gholami, D., Mahmoodi, B., & Jafari, A. (2020). Spatial modeling the probability of wildfire occurrence using frequency ratio and weight- of-evidence models. Iranian Journal of Forest and Range Protection Research, 17(2), 125-144. (In persian)
Palialexis, A., Georgakarakos, S., Lika, K., & Valavanis, V. D. (2009). Comparing novel approaches used for prediction of species distribution from presence/absence acoustic data. In Proceedings of the Second International Conference on Environmental Management, Engineering, Planning and Economics (CEMEPE 09).
Phillips, S.J., Anderson, R.P., & Schapire, R.E. (2006). Maximum entropy modeling ofspecies geographic distributions. Ecological modelling, 190, 231-259.
Pourghasemi, H.R., Gayen, A., Edalat, M., Zarafshar, M., & Tiefenbacher, J.P. (2020b). Is multi-hazard mapping effective in assessing natural hazards and integrated watershed management?. Geoscience Frontiers, 11(4), 1203-1217. (In persian)
Pourghasemi, H.R., Kariminejad, N., Amiri, M., Edalat, M., Zarafshar, M., Blaschke, T., & Cerda, A. (2020a). Assessing and mapping multi-hazard risk susceptibility using a machine learning technique. Scientific reports, 10(1), 3203. (In persian)
Pouyan, S., Khojandi, K., & Pourghasemi, H. (2022). Spatial modeling of forest fire susceptibility in Khuzestan Province, The 17th National Conference on Watershed Management Sciences and Engineering of Iran with a Focus on Watershed Management and Sustainable Food Security, Jiroft.
https://civilica.com/doc/1623688 (in Persian)
Renard, Q., Pélissier, R., Ramesh, B.R., & Kodandapani, N. (2012). Environmental susceptibility model for predicting forest fire occurrence in the Western Ghats of India. International Journal of Wildland Fire, 21(4), 368-379.
Setiawan, I., Mahmud, A.R., Mansor, S., Mohamed Shariff, A.R. & Nuruddin, A.A. (2004). GIS‐ grid‐based and multi‐criteria analysis for identifying and mapping peat swamp forest fire hazard in Pahang, Malaysia. Disaster Prevention and Management, 13(5), 379-386.
Somashekar, R., Ravikumar, P., Mohankumar, C., Prakash, K., & Nagaraja, B. (2009). Burnt area mapping of Bandipur National Park, India using IRS1C/1D LISS III data. Journal of the Indian Society of Remote Sensing, 37, 37-50.