Using neural networks and a fuzzy inference system to evaluate the risk of wildfires and the pinpointing of firefighting stations in forests on the northern slopes of the Zagros Mountains, Iran (case study: Shimbar national wildlife preserve)

Author:

Salehi Nafieh,Dashti Soolmaz,Roshan Sina Attar,Nazarpour Ahad,Jaafarzadeh Neamatollah

Publisher

Springer Science and Business Media LLC

Subject

Management, Monitoring, Policy and Law,Pollution,General Environmental Science,General Medicine

Reference48 articles.

1. Aali Mahmoudi Sarab S., Fiqhi, J., Jabarian Amiri, B., Danehkar, A., & Atarod, P. (2013). Assessment of climatic elements effective in the development of Zagros wildfires using regression models in the Zagros Forests of Izeh County. Journal of Natural Environment Iranian Journal of Natural Resources, 66(2), 75-86. http://ijae.iut.ac.ir/article-1-188-fa.html

2. Al-Bashiti, M. K., & Naser, M. Z. (2022). Machine learning for wildfire classification: Exploring blackbox, eXplainable, symbolic, and SMOTE methods. Natural Hazards Research, 2(3), 154-165. https://doi.org/10.1016/j.nhres.2022.08.001

3. Amiri, T., Banj Shafiei, A., Erfanian, M., Hosseinzadeh, O., & Beygiheidarlou, H. (2018). Locating suitable areas for forest fire fighting stations in Sardasht NW Iran. Iranian Journal of Forest, 10(3), 319–335.

4. Anderson-Bell, J., Schillaci, C., & Lipani, A. (2021). Predicting non-residential building fire risk using geospatial information and convolutional neural networks. Remote Sensing Applications: Society and Environment, 21. https://doi.org/10.1016/j.rsase.2021.100470

5. Barrosa, k., Ribeiroa, C., Marcattia, G., Lorenzona, L., Castroa, N., Carvalhob, G., & Santosb, A. (2018). Markov chains and cellular automata to predict environments subject to desertification. Journal of Environmental Management, 225, 160–167. https://doi.org/10.1016/j.jenvman.2018.07.064

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