arXiv:2601.03327v3 Announce Type: replace Abstract: Wildfires are highly imbalanced natural hazards in both space and severity, making the prediction of extreme events particularly challenging. In this work, we introduce the first ordinal classification framework for forecasting wildfire severity levels directly aligned with operational decision-making in France. Our study investigates the influence of loss-function design on the ability of neural models to predict rare yet critical high-severity fire occurrences. We compare standard cross-entropy with several ordinal-aware objectives, includi
The increasing frequency and intensity of wildfires globally, exacerbated by climate change, are driving urgent demand for more effective prediction and mitigation strategies.
Improved wildfire prediction, especially for extreme events, offers critical tools for operational decision-making, asset protection, public safety, and resource allocation in affected regions.
The development of ordinal classification frameworks and specialized loss functions enhances the precision and utility of AI models in forecasting wildfire severity, moving beyond general predictions to actionable insights.
- · Emergency Services
- · Forestry Management
- · Insurance Industry
- · AI/ML Developers
- · Regions Prone to Wildfires (without advanced prediction systems)
More accurate and timely wildfire severity predictions enable targeted and efficient deployment of firefighting resources.
Reduced economic losses and environmental damage from wildfires due to improved preparedness and response capabilities facilitated by AI.
Integration of similar ordinal classification frameworks into other natural disaster prediction models, leading to a broader revolution in disaster management.
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Read at arXiv cs.LG