Comparison of diverse machine learning algorithms for forest fire susceptibility mapping in Antalya, Türkiye

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dc.contributor.authorAlkan Akıncı, Hazan
dc.contributor.authorAkıncı, Halil
dc.contributor.authorZeybek, Mustafa
dc.date.accessioned2024-11-21T08:43:54Z
dc.date.available2024-11-21T08:43:54Z
dc.date.issued2024
dc.departmentAÇÜ, Mühendislik Fakültesi, Harita Mühendisliği Bölümüen_US
dc.departmentAÇÜ, Orman Fakültesi, Orman Mühendisliği Bölümüen_US
dc.description.abstractAntalya is one of the provinces with the highest number of forest fires in Türkiye. In 2021, 278 forest fires occurred within the administrative boundaries of Antalya Regional Directorate of Forestry. The main objective of this study is to produce forest fire susceptibility (FFS) maps of Antalya province using machine learning (ML) models. In addition to forest fire inventory data, 16 factors, including topographic, environmental, meteorological, and human-driven, were used in the study. Inventory data included 2166 fire ignition points from the General Directorate of Forestry. 70 % of the inventory dataset was used to train the ML models and 30 % to validate the models. Overall accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC) approaches were considered as validation metrics. FFS maps of Antalya were produced using stand-alone ML algorithms, K-Nearest Neighbors, and Support Vector Machines, as well as tree-based Conditional Inference Trees (CTREE), Random Forest (RF), Gradient Boosting Machines (GBM), and Extreme Gradient Boosting (XGBoost) algorithms. To the best of our knowledge, this is the first study using the CTREE algorithm for forest fire susceptibility mapping. Therefore, this study is important for the related literature. The validation results revealed that the XGBoost model outperformed other models. It is thought that the FFS map produced using the XGBoost model will guide forest engineers, wildland firefighting teams, and firefighters to minimize damage and control forest fires.
dc.identifier.doi10.1016/j.asr.2024.04.018
dc.identifier.endpage667en_US
dc.identifier.issn0273-1177
dc.identifier.issue2en_US
dc.identifier.scopusqualityQ1
dc.identifier.startpage647en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.asr.2024.04.018
dc.identifier.urihttps://hdl.handle.net/11494/4987
dc.identifier.volume74en_US
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherElsevier Ltden_US
dc.relation.ispartofAdvances in Space Research
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.subjectAntalyaen_US
dc.subjectForest Fireen_US
dc.subjectGISen_US
dc.subjectMachine Learning Algorithmsen_US
dc.subjectSusceptibility Mappingen_US
dc.titleComparison of diverse machine learning algorithms for forest fire susceptibility mapping in Antalya, Türkiyeen_US
dc.typeArticle

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