Machine learning based forest fire susceptibility assessment of Manavgat district (Antalya), Turkey
Author:
Funder
Artvin Çoruh Üniversitesi
Publisher
Springer Science and Business Media LLC
Subject
General Earth and Planetary Sciences
Link
https://link.springer.com/content/pdf/10.1007/s12145-023-00953-5.pdf
Reference92 articles.
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2. Abedi Gheshlaghi HA, Feizizadeh B, Blaschke T (2020) GIS-based forest fire risk mapping using the analytical network process and fuzzy logic. J Environ Plan Manag 63(3):481–499. https://doi.org/10.1080/09640568.2019.1594726
3. Achu AL, Thomas J, Aju CD, Gopinath G, Kumar S, Reghunath R (2021) Machine-learning modelling of fire susceptibility in a forest-agriculture mosaic landscape of southern India. Ecol Inf 64:101348. https://doi.org/10.1016/j.ecoinf.2021.101348
4. Adab H, Kanniah KD, Solaimani K (2013) Modeling forest fire risk in the northeast of Iran using remote sensing and GIS techniques. Nat Hazards 65(3):1723–1743. https://doi.org/10.1007/s11069-012-0450-8
5. Aditian A, Kubota T, Shinohara Y (2018) Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon. Indonesia Geomorphology 318:101–111. https://doi.org/10.1016/j.geomorph.2018.06.006
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