Application of remote sensing and machine learning algorithms for forest fire mapping in a Mediterranean area

•Five new ensemble models were developed for forest fire susceptibility modeling.•10 conditioning factors were considered in this study.•RF-FR ensemble model outperformed SVM-FR, MLP-FR, CART-FR and LR-FR models. Forest fire disaster is currently the subject of intense research worldwide. The develo...

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Vydané v:	Ecological indicators Ročník 129; s. 107869
Hlavní autori:	Mohajane, Meriame, Costache, Romulus, Karimi, Firoozeh, Bao Pham, Quoc, Essahlaoui, Ali, Nguyen, Hoang, Laneve, Giovanni, Oudija, Fatiha
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 01.10.2021 Elsevier
Predmet:	Forest fire forest fires forests Hybrid machine learning algorithm hybrids inventories land use Mediterranean area Mediterranean region Morocco prediction rain regression analysis Remote sensing risk temperature wind speed Morocco Mediterranean region Mediterranean area Forest fire Remote sensing Hybrid machine learning algorithm
ISSN:	1470-160X, 1872-7034
On-line prístup:	Získať plný text
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