| Yazarlar |
Doç. Dr. Kemal AKYOL
Kastamonu Üniversitesi, Türkiye |
| Özet |
| Forests 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 abstract: Block diagram of the proposed model [Figure not available: see fulltext.]. |
| Anahtar Kelimeler |
| Bi-directional long short-term memory | Computer vision | Deep learning | Forest fire | Stacking ensemble model |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayımlanan tam makale |
| Dergi Adı | International Journal of Environmental Science and Technology |
| Dergi ISSN | 1735-1472 |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Dergi Grubu | Q3 |
| Makale Dili | İngilizce |
| Basım Tarihi | 09-2023 |
| Cilt No | 20 |
| Sayfalar | 13245 / 13258 |
| Doi Numarası | 10.1007/s13762-023-05194-z |
| Makale Linki | https://doi.org/10.1007/s13762-023-05194-z |
| Atıf Sayıları |
