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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43410%2F21%3A43918942" target="_blank" >RIV/62156489:43410/21:43918942 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.3390/f12010005" target="_blank" >https://doi.org/10.3390/f12010005</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/f12010005" target="_blank" >10.3390/f12010005</a>

Result language

angličtina

Original language name

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

Original language description

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&apos;s area.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

40102 - Forestry

Project

—

Continuities

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Forests

ISSN

1999-4907

e-ISSN

—

Volume of the periodical

12

Issue of the periodical within the volume

1

Country of publishing house

CH - SWITZERLAND

Number of pages

17

Pages from-to

5

UT code for WoS article

000610210900001

EID of the result in the Scopus database

2-s2.0-85099476651