A performance comparison of machine learning models for wildfire occurrence risk prediction in the Brazilian Federal District region
Author:
Funder
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Publisher
Springer Science and Business Media LLC
Subject
General Environmental Science
Link
https://link.springer.com/content/pdf/10.1007/s10669-023-09921-2.pdf
Reference50 articles.
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2. Balch JK, Bradley BA, Abatzoglou JT, Nagy RC, Fusco EJ, Mahood AL, Williams AP, Cattau ME, Lutz JA, Hegewisch KC (2017) Human-started wildfires expand the fire niche across the United States. Proc Natl Acad Sci USA 114(11):2946–2951
3. Coelho MP, Alvarado EC, Fidelis A, Lima J, Vieira S (2016) Fire management for conservation in Brazilian savannas: a review. Int J Wildland Fire 25(7):711–722
4. Davis J, Goadrich M, “The relationship between precision-recall and roc curves,” in Proceedings of the 23rd international conference on machine learning, 2006, pp. 233–240.
5. de Bem PP, de Carvalho Junior OA, Matricardi EAT, Guimaraed RF, Gomes RAT (2019) Predicting wildfire vulnerability using logistic regression and artificial neural networks: a case study in Brazil’s Federal District. Int J Wildland Fire 28(1):35–45
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