dc.contributor.author
Alkhatib, Ramez
dc.contributor.author
Sahwan, Wahib
dc.contributor.author
Alkhatieb, Anas
dc.contributor.author
Schütt, Brigitta
dc.date.accessioned
2023-08-31T10:26:37Z
dc.date.available
2023-08-31T10:26:37Z
dc.identifier.uri
https://refubium.fu-berlin.de/handle/fub188/40620
dc.identifier.uri
http://dx.doi.org/10.17169/refubium-40341
dc.description.abstract
Due to the harm forest fires cause to the environment and the economy as they occur more frequently around the world, early fire prediction and detection are necessary. To anticipate and discover forest fires, several technologies and techniques were put forth. To forecast the likelihood of forest fires and evaluate the risk of forest fire-induced damage, artificial intelligence techniques are a crucial enabling technology. In current times, there has been a lot of interest in machine learning techniques. The machine learning methods that are used to identify and forecast forest fires are reviewed in this article. Selecting the best forecasting model is a constant gamble because each ML algorithm has advantages and disadvantages. Our main goal is to discover the research gaps and recent studies that use machine learning techniques to study forest fires. By choosing the best ML techniques based on particular forest characteristics, the current research results boost prediction power.
en
dc.format.extent
15 Seiten
dc.rights.uri
https://creativecommons.org/licenses/by/4.0/
dc.subject
machine learning
en
dc.subject
forest fires
en
dc.subject
deep learning
en
dc.subject
remote sensing
en
dc.subject
Google Earth Engine (GEE)
en
dc.subject.ddc
500 Naturwissenschaften und Mathematik::550 Geowissenschaften, Geologie::550 Geowissenschaften
dc.subject.ddc
900 Geschichte und Geografie::910 Geografie, Reisen::910 Geografie, Reisen
dc.title
A Brief Review of Machine Learning Algorithms in Forest Fires Science
dc.type
Wissenschaftlicher Artikel
dcterms.bibliographicCitation.articlenumber
8275
dcterms.bibliographicCitation.doi
10.3390/app13148275
dcterms.bibliographicCitation.journaltitle
Applied Sciences
dcterms.bibliographicCitation.number
14
dcterms.bibliographicCitation.originalpublishername
MDPI
dcterms.bibliographicCitation.volume
13
dcterms.bibliographicCitation.url
https://doi.org/10.3390/app13148275
refubium.affiliation
Geowissenschaften
refubium.affiliation.other
Institut für Geographische Wissenschaften, Physische Geographie
![This authority value has been confirmed as accurate by an interactive user](/cache_202e45ad85b55efaeb29160f63cd3f3b/themes/FuCD/images/authority_control/invisible.gif)
refubium.note.author
Die Publikation wurde aus Open Access Publikationsgeldern der Freien Universität Berlin gefördert.
de
refubium.resourceType.isindependentpub
no
dcterms.accessRights.openaire
open access
dcterms.isPartOf.eissn
2076-3417