iForest - Biogeosciences and Forestry


Forest fire occurrence modeling in Southwest Turkey using MaxEnt machine learning technique

Merih Göltas (1), Hamit Ayberk (1), Omer Kücük (2)

iForest - Biogeosciences and Forestry, Volume 17, Issue 1, Pages 10-18 (2024)
doi: https://doi.org/10.3832/ifor4321-016
Published: Feb 02, 2024 - Copyright © 2024 SISEF

Research Articles

Climate anomalies and potential increased human pressure will likely cause the increase in frequency and damage of forest fires in the near future. Therefore, accurately and temporally estimating and mapping forest fire probability is necessary for preventing from destructive effects of forest fires. In this study, the forest fire occurrence in Southwestern Turkey was modeled and mapped with the maximum entropy (MaxEnt) approach. We used past fire locations (from 2008 to 2018) with environmental variables such as fuel type, topography, meteorological parameters, and human activity for modeling and mapping, using data that could be obtained quickly and easily. The performances of fire occurrence models was quite satisfactory (AUC: range from 0.71 to 0.87) in terms of the model reliability. When the fire occurrence models were analyzed in detail, it was seen that the environmental variables with the highest gain when used alone were the maximum temperature, tree species composition, and distance to agricultural lands. To evaluate the models, we compared the fire locations between 2019 and 2020 with those on reclassified fire probability maps. Fire location from 2019-2020 fit substantially within the model fire occurrence predictions since many fire points in high or extreme fire probability categories has been observed. The results of this study can be a guideline for the Mediterranean forestry that has consistently struggled the forest fires and attempted to manage effectively forest lands at fire risk.


Turkey, Fire Ignition, Fire Risk, Maximum Entropy, Machine Learning

Authors’ address

Merih Göltas 0000-0002-6052-5373
Hamit Ayberk 0000-0002-6896-264X
Istanbul University-Cerrahpasa, Faculty of Forestry, Department of Forest Engineering, 34473, Istanbul (Turkey)
Omer Kücük 0000-0003-2639-8195
Kastamonu University, Faculty of Forestry, Department of Forest Engineering, 37100, Kastamonu (Turkey)

Corresponding author


Göltas M, Ayberk H, Kücük O (2024). Forest fire occurrence modeling in Southwest Turkey using MaxEnt machine learning technique. iForest 17: 10-18. - doi: 10.3832/ifor4321-016

Academic Editor

Davide Ascoli

Paper history

Received: Feb 02, 2023
Accepted: Nov 06, 2023

First online: Feb 02, 2024
Publication Date: Feb 29, 2024
Publication Time: 2.93 months

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