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Forest fire occurrence modeling in Southwest Turkey using MaxEnt machine learning technique     
Yazarlar
Merih Göltaş
Türkiye
Hamit Ayberk
Türkiye
Prof. Dr. Ömer KÜÇÜK Prof. Dr. Ömer KÜÇÜK
Kastamonu Üniversitesi, Türkiye
Özet
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 re-classified 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.
Anahtar Kelimeler
Fire Ignition | Fire Risk | Machine Learning | Maximum Entropy | Turkey
Makale Türü Özgün Makale
Makale Alt Türü SSCI, AHCI, SCI, SCI-Exp dergilerinde yayımlanan tam makale
Dergi Adı IFOREST-BIOGEOSCIENCES AND FORESTRY
Dergi ISSN 1971-7458
Dergi Tarandığı Indeksler SCI-Expanded
Dergi Grubu Q2
Makale Dili İngilizce
Basım Tarihi 02-2024
Cilt No 17
Sayı 1
Sayfalar 10 / 18
Doi Numarası 10.3832/ifor4321-016
Makale Linki http://dx.doi.org/10.3832/ifor4321-016
BM Sürdürülebilir Kalkınma Amaçları
Atıf Sayıları
Forest fire occurrence modeling in Southwest Turkey using MaxEnt machine learning technique

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