Machine learning based forest fire susceptibility assessment of Manavgat district (Antalya), Turkey

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dc.authoridHazan Alkan Akıncı / 0000-0002-5618-289Xen_US
dc.authoridHalil Akıncı / 0000-0002-9957-1692en_US
dc.contributor.authorAlkan Akıncı, Hazan
dc.contributor.authorAkıncı, Halil
dc.date.accessioned2023-02-23T07:58:02Z
dc.date.available2023-02-23T07:58:02Z
dc.date.issued2023
dc.departmentAÇÜ, Orman Fakültesi, Orman Mühendisliği Bölümüen_US
dc.description.abstractThis study primarily aims to produce forest fire susceptibility maps for the Manavgat district of Antalya province in Turkey using different machine learning (ML) techniques. Forest fire inventory data were obtained from the General Directorate of Forestry. The inventory data comprise a total of 545 forest fire ignition points during the years 2013–2021. For model training and validation, 70% and 30% of these points were used, respectively. Average annual temperature, average annual rainfall, aspect, distance to rivers, elevation, distance to settlements, forest type, distance to roads, land cover, plan curvature, slope, solar radiation, tree cover density, topographic wetness index, and wind effect parameters were used in the study. Multicollinearity analysis of these 15 factors showed that they are independent of each other. Treebased ML models, namely, eXtreme gradient boosting (XGBoost), random forest, and gradient boosting machine, as well as artificial neural networks (ANN) were used to produce forest fire susceptibility maps. The metrics of overall accuracy, precision, recall, F1 score and area under the receiver operating characteristic curve (AU-ROC) were used to evaluate the performance of the ML models. Based on our results, the XGBoost model revealed the most appropriate susceptibility map that could be used for fire prevention measures.
dc.identifier.citationAkıncı, H. A., & Akıncı, H. (2023). Machine learning based forest fire susceptibility assessment of Manavgat district (Antalya), Turkey. Earth Science Informatics, 16(1), 397–414. ‌en_US
dc.identifier.doi10.1007/s12145-023-00953-5
dc.identifier.endpage414en_US
dc.identifier.scopusqualityQ2
dc.identifier.startpage397en_US
dc.identifier.urihttps://doi.org/10.1007/s12145-023-00953-5
dc.identifier.urihttps://hdl.handle.net/11494/4716
dc.identifier.volume16en_US
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorAlkan Akıncı, Hazan
dc.institutionauthorAkıncı, Halil
dc.language.isoenen_US
dc.publisherSpringer Linken_US
dc.relation.ispartofEarth Science Informatics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectForest firesen_US
dc.subjectSusceptibility mappingen_US
dc.subjectMachine learningen_US
dc.subjectGISen_US
dc.subjectManavgaten_US
dc.titleMachine learning based forest fire susceptibility assessment of Manavgat district (Antalya), Turkeyen_US
dc.typeArticle

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