FOREST FIRE SUSCEPTIBILITY ASSESSMENT WITH MACHINE LEARNING METHODS IN NORTH-EAST TURKIYE

8 citations as recorded by crossref.

1. Temporal–Spatial Characteristics and Influencing Factors of Forest Fires in the Tropic of Cancer (Yunnan Section) H. Xu et al. 10.3390/f15040661
2. Mapping climate change induced forest fire susceptibility using tree-based machine learning algorithms in Bangladesh M. Haydar et al. 10.1016/j.ecolind.2025.113657
3. Modeling of Forest Fire Risk Areas of Amazonas Department, Peru: Comparative Evaluation of Three Machine Learning Methods A. Vergara et al. 10.3390/f16020273
4. Assessment of large-scale multiple forest disturbance susceptibilities with AutoML framework: an Izmir Regional Forest Directorate case R. Eker et al. 10.1007/s11676-024-01723-9
5. Forest fire probability zonation using dNBR and machine learning models: a case study at the Similipal Biosphere Reserve (SBR), Odisha, India R. Guria et al. 10.1007/s11356-025-35976-6
6. Machine learning-based forest fire susceptibility mapping of Southern Mizoram, a part of Indo-Burma Biodiversity Hotspot P. Gupta et al. 10.1007/s11356-025-36621-y
7. Application of Artificial Neural Network in Wildfire Early Prediction Systems H. Nikova et al. 10.37394/232015.2023.19.128
8. Integrating geospatial, remote sensing, and machine learning for climate-induced forest fire susceptibility mapping in Similipal Tiger Reserve, India C. Singha et al. 10.1016/j.foreco.2024.121729