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Article

Forest Fire Probability Mapping in Eastern Serbia: Logistic Regression versus Random Forest Method

1
Chair of Forest Protection, University of Belgrade Faculty of Forestry, 11030 Belgrade, Serbia
2
Department of Forest Protection and Wildlife Management, Faculty of Forestry and Wood Technology, Mendel University in Brno, 61300 Brno, Czech Republic
3
State Enterprise “Srbijašume“, 11000 Belgrade, Serbia
4
Military Academy, University of Defence in Belgrade, 11000 Belgrade, Serbia
5
Center for Predictive Analytics, 11000 Belgrade, Serbia
6
Department for Biomedical Engineering and Biophysics, University of Belgrade Institute for Medical Research, 11000 Belgrade, Serbia
*
Author to whom correspondence should be addressed.
Forests 2021, 12(1), 5; https://doi.org/10.3390/f12010005
Received: 24 November 2020 / Revised: 16 December 2020 / Accepted: 17 December 2020 / Published: 22 December 2020
(This article belongs to the Special Issue Forest Fire Risk Prediction)
Forest fire risk has increased globally during the previous decades. The Mediterranean region is traditionally the most at risk in Europe, but continental countries like Serbia have experienced significant economic and ecological losses due to forest fires. To prevent damage to forests and infrastructure, alongside other societal losses, it is necessary to create an effective protection system against fire, which minimizes the harmful effects. Forest fire probability mapping, as one of the basic tools in risk management, allows the allocation of resources for fire suppression, within a fire season, from zones with a lower risk to those under higher threat. Logistic regression (LR) has been used as a standard procedure in forest fire probability mapping, but in the last decade, machine learning methods such as fandom forest (RF) have become more frequent. The main goals in this study were to (i) determine the main explanatory variables for forest fire occurrence for both models, LR and RF, and (ii) map the probability of forest fire occurrence in Eastern Serbia based on LR and RF. The most important variable was drought code, followed by different anthropogenic features depending on the type of the model. The RF models demonstrated better overall predictive ability than LR models. The map produced may increase firefighting efficiency due to the early detection of forest fire and enable resources to be allocated in the eastern part of Serbia, which covers more than one-third of the country’s area. View Full-Text
Keywords: occurrence of forest fire; machine learning; variable importance; prediction accuracy occurrence of forest fire; machine learning; variable importance; prediction accuracy
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MDPI and ACS Style

Milanović, S.; Marković, N.; Pamučar, D.; Gigović, L.; Kostić, P.; Milanović, S.D. Forest Fire Probability Mapping in Eastern Serbia: Logistic Regression versus Random Forest Method. Forests 2021, 12, 5. https://doi.org/10.3390/f12010005

AMA Style

Milanović S, Marković N, Pamučar D, Gigović L, Kostić P, Milanović SD. Forest Fire Probability Mapping in Eastern Serbia: Logistic Regression versus Random Forest Method. Forests. 2021; 12(1):5. https://doi.org/10.3390/f12010005

Chicago/Turabian Style

Milanović, Slobodan, Nenad Marković, Dragan Pamučar, Ljubomir Gigović, Pavle Kostić, and Sladjan D. Milanović 2021. "Forest Fire Probability Mapping in Eastern Serbia: Logistic Regression versus Random Forest Method" Forests 12, no. 1: 5. https://doi.org/10.3390/f12010005

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