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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Veröffentlicht in:	Ecological indicators Jg. 129; S. 107869
Hauptverfasser:	Mohajane, Meriame, Costache, Romulus, Karimi, Firoozeh, Bao Pham, Quoc, Essahlaoui, Ali, Nguyen, Hoang, Laneve, Giovanni, Oudija, Fatiha
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.10.2021 Elsevier
Schlagworte:	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
Online-Zugang:	Volltext
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