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Analyzes wildfire forecasting complexity using machine learning, indicating advanced methods are needed.
This article examines the complexity of probabilistic wildfire forecasting using machine learning. A training set including meteorological, satellite, and historical data is analyzed, distributions are visualized, and dimensionality reduction is performed using nonlinear t-SNE and UMAP methods. It is demonstrated that the "fire" and "no fire" classes significantly overlap in the feature space, suggesting the need for complex, nonlinear machine learning methods.
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Andrey Goncharov (2026) studied this question.
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