Akyol, K.2023-10-232023-10-232023.01.011735-1472https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=dspace_ku&SrcAuth=WosAPI&KeyUT=WOS:001069226000005&DestLinkType=FullRecord&DestApp=WOShttps://hdl.handle.net/20.500.12597/17806Forests reduce soil erosion and prevent drought, wind, and other natural disasters. Forest fires, which threaten millions of hectares of forest area yearly, destroy these precious resources. This study aims to design a deep learning model with high accuracy to intervene in forest fires at an early stage. A stacked-based ensemble learning model is proposed for fire detection from forest landscape images in this context. This model offers high test accuracies of 97.37%, 95.79%, and 95.79% with hold-out validation, fivefold cross-validation, and tenfold cross-validation experiments, respectively. The artificial intelligence model developed in this study could be used in real-time systems run on unmanned aerial vehicles to prevent potential disasters in forest areas.Graphical abstractBlock diagram of the proposed model.eninfo:eu-repo/semantics/openAccessForest fre, Computer vision, Deep learning, Stacking ensemble model, Bi-directional long short-term, memoryArticle10.1007/s13762-023-051940010692260000051735-2630