Forest fire mapping: a comparison between GIS-based random forest and Bayesian models
Author:
Funder
College of Agriculture, Shiraz University
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11069-024-06457-9.pdf
Reference80 articles.
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2. Achu AL, Thomas J, Aju CD et al (2021) Machine-learning modelling of fire susceptibility in a forest-agriculture mosaic landscape of southern India. Ecol Inform 64:101348. https://doi.org/10.1016/j.ecoinf.2021.101348
3. Adab H, Kanniah KD, Solaimani K (2013) Modeling forest fire risk in the northeast of Iran using remote sensing and GIS techniques. Nat Hazards 65:1723–1743
4. Aertsen W, Kint V, Van Orshoven J, Muys B (2011) Evaluation of modelling techniques for forest site productivity prediction in contrasting ecoregions using stochastic multicriteria acceptability analysis (SMAA). Environ Model Softw 26:929–937
5. Aertsen W, Kint V, de Vos B et al (2012) Predicting forest site productivity in temperate lowland from forest floor, soil and litterfall characteristics using boosted regression trees. Plant Soil 354:157–172. https://doi.org/10.1007/s11104-011-1052-z
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