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dc.contributor.author
Kantarcioglu, Omer
dc.contributor.author
Kocaman, Sultan
dc.contributor.author
Schindler, Konrad
dc.date.accessioned
2023-03-21T07:22:26Z
dc.date.available
2023-03-21T04:13:05Z
dc.date.available
2023-03-21T07:22:26Z
dc.date.issued
2023-07
dc.identifier.issn
1574-9541
dc.identifier.issn
1878-0512
dc.identifier.other
10.1016/j.ecoinf.2023.102034
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/604169
dc.description.abstract
Wildfires often threaten natural and economic resources and human lives. Wildfire susceptibility assessments have become essential for efficient disaster management and increasing resilience. In this study, we assessed the forest fire susceptibility in Istanbul Province and Thrace Region, Türkiye using a well-known machine learning technique, Artificial Neural Networks (ANN). Benefiting from freely available Earth Observation datasets such as Sentinel-2 images, Tree Cover Density from European Union (EU) European Environment Agency (EEA) Copernicus Land Monitoring Service, Shuttle Radar Topography Mission (SRTM) data, etc., and a forest inventory with ignition locations recorded over a period of eight years, we utilized a total of 16 independent and one dependent variables. The variables can be categorized as anthropogenic, topographic, vegetation, and hydrological factors. A ratio of 1:2 was preferred for the fire/non-fire location samples. The results show that the ANN exhibited high prediction performance with Area Under the Receiver Operating Characteristic Curve (AUC) value and F-1 score of 0.94 and 0.80, respectively. Based on feature importance analyses, we found that a human-related factor, proximity to forest roads, was the most predictive input variable. The ANN model trained with openly available data (i.e., without forest database) also yielded a high F-1 score, but produced maps with fewer details. Our results confirm that data-driven machine learning methods are promising for regional forest fire susceptibility assessments and can be extended further for other regions by deriving similar parameters from freely available Earth Observation datasets.
en_US
dc.language.iso
en
en_US
dc.publisher
Elsevier
en_US
dc.subject
Forest fire susceptibility assessment
en_US
dc.subject
Artificial neural network
en_US
dc.subject
Earth observation
en_US
dc.subject
Spectral indices
en_US
dc.subject
Forest inventory
en_US
dc.title
Artificial neural networks for assessing forest fire susceptibility in Türkiye
en_US
dc.type
Journal Article
dc.date.published
2023-02-26
ethz.journal.title
Ecological Informatics
ethz.journal.volume
75
en_US
ethz.pages.start
102034
en_US
ethz.size
17 p.
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Amsterdam
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02115 - Dep. Bau, Umwelt und Geomatik / Dep. of Civil, Env. and Geomatic Eng.::02647 - Inst. f. Geodäsie und Photogrammetrie / Institute of Geodesy and Photogrammetry::03886 - Schindler, Konrad / Schindler, Konrad
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02115 - Dep. Bau, Umwelt und Geomatik / Dep. of Civil, Env. and Geomatic Eng.::02647 - Inst. f. Geodäsie und Photogrammetrie / Institute of Geodesy and Photogrammetry::03886 - Schindler, Konrad / Schindler, Konrad
ethz.date.deposited
2023-03-21T04:13:08Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2023-03-21T07:22:27Z
ethz.rosetta.lastUpdated
2024-02-02T21:13:19Z
ethz.rosetta.versionExported
true
ethz.COinS
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