2022
DOI: 10.1016/j.ecoinf.2022.101647
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Machine learning based wildfire susceptibility mapping using remotely sensed fire data and GIS: A case study of Adana and Mersin provinces, Turkey

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Cited by 87 publications
(39 citation statements)
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“…The occurrence of fire at lower and moderate slopes may be a result of the somewhat homogeneous nature of the forested area. A recent study used machine learning to map wildfire susceptibility using remotely sensed fire data and GIS in Adana and Mersin provinces, Turkey explained that elevation, temperature, and slope factors were the most contributing factors (Iban & Sekertekin, 2022).…”
Section: Discussionmentioning
confidence: 99%
“…The occurrence of fire at lower and moderate slopes may be a result of the somewhat homogeneous nature of the forested area. A recent study used machine learning to map wildfire susceptibility using remotely sensed fire data and GIS in Adana and Mersin provinces, Turkey explained that elevation, temperature, and slope factors were the most contributing factors (Iban & Sekertekin, 2022).…”
Section: Discussionmentioning
confidence: 99%
“…Also, a value of zero shows that the surface is flat at that cell. In wildfire studies the wetness of the ground is important, therefore, TWI has been measured as well, which is calculated based on (1) [29]:…”
Section: Data Usedmentioning
confidence: 99%
“…Therefore, several studies have examined them for modeling the FSMs in forest areas worldwide. Iban et al (Iban and Sekertekin, 2022) evaluated seven ML classifiers, including random forest (RF), AdaBoost (AB), and support vector machine (SVM), for modeling FSMs for two provinces of Adana and Mersin in Turkey. In their accuracy assessment process, the lowest and highest accuracy scores for the applied seven different ML classifiers were 0.734 and 0.812.…”
Section: Introductionmentioning
confidence: 99%