Deep Learning Models for Forest Fire Prediction: Insights into Feature Selection for Climate- Resilient Forestry
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Date
2025-11-25
Journal Title
Journal ISSN
Volume Title
Publisher
Journal of Sustainable Forestry,
Abstract
This scoping review investigates feature selection and integration
practices in deep learning models for forest-fire prediction, emphasiz-
ing their impact on model performance and sustainable forest man-
agement. Following PRISMA guidelines, 36 peer-reviewed studies
published between 2020 and 2025 were systematically synthesized.
The analysis identifies commonly adopted features, topographical,
climatic, vegetation, and anthropogenic, and examines how their
selection influences predictive accuracy, interpretability, and contex-
tual relevance. Results show that hybrid architectures such as CNN-
LSTM and U-Net variants effectively capture spatial and temporal fire
dynamics. Feature selection techniques, including correlation analysis,
SHAP, Relief-F, and Information Gain, improved model robustness and
transparency. Challenges such as class imbalance, high-dimensional
data, and inconsistent temporal resolution were addressed using
advanced preprocessing, dimensionality reduction, attention mechan-
isms, and explainable AI. However, gaps persist, particularly in under-
represented regions, regarding standardized, context-aware feature
integration. The study highlights the importance of incorporating
socio-environmental indicators, such as land use and human activity,
to reflect real-world fire triggers. Multi-source data fusion emerged as
critical for improving generalizability and operational deployment.
This review emphasizes deep learning’s potential to advance sustain-
able forest management, support climate mitigation, and foster resi-
lient ecosystems and communities. It also emphasizes its alignment
with SDG 13: Climate action and SDG 15: Life on land.
Sustainable Development Goals
SDG 13: Climate Action
SDG 15: Life on Land
Keywords
Deep learning, forest fire prediction, feature selection, sustainable forestry, climate change adaptation