Robust stacking-based ensemble learning model for forest fire detection
Yazarlar (1)
Prof. Dr. Kemal AKYOL Kastamonu Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | International Journal of Environmental Science and Technology (Q2) | ||
| Dergi ISSN | 1735-1472 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 09-2023 |
| Kabul Tarihi | 24-08-2023 | Yayınlanma Tarihi | 19-09-2023 |
| Cilt / Sayı / Sayfa | 20 / 12 / 13245–13258 | DOI | 10.1007/s13762-023-05194-z |
| Makale Linki | https://doi.org/10.1007/s13762-023-05194-z | ||
| 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 abstractBlock diagram of the proposed model |
| Anahtar Kelimeler |
| Bi-directional long short-term memory | Computer vision | Deep learning | Forest fire | Stacking ensemble model |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Scopus | 5 |
| Google Scholar | 6 |