arXiv:2509.25017v2 Announce Type: replace Abstract: Wildfires are among the most severe natural hazards, posing a significant threat to both humans and natural ecosystems. The growing risk of wildfires increases the demand for forecasting models that are not only accurate but also reliable. Deep Learning (DL) has shown promise in predicting wildfire danger; however, its adoption is hindered by concerns over the reliability of its predictions, some of which stem from the lack of uncertainty quantification. To address this challenge, we present an uncertainty-aware DL framework that jointly capt
The increasing frequency and intensity of wildfires globally, coupled with advancements in deep learning, are driving the urgent need for more reliable forecasting methods.
Reliable wildfire forecasting with uncertainty quantification can significantly improve disaster preparedness, resource allocation, and mitigation strategies, directly impacting human safety and environmental protection.
The ability to quantify uncertainty in AI-driven wildfire predictions moves deep learning applications beyond just accuracy metrics, addressing a key barrier to broader adoption in critical safety domains.
- · Emergency Services
- · Insurance Companies
- · Environmental Agencies
- · Deep Learning Researchers
- · Regions Prone to Wildfires (without advanced forecasting)
- · Traditional Forecasting Models
Improved wildfire management and reduced damage to property and ecosystems.
Increased public trust and broader adoption of AI in other critical environmental monitoring and disaster prediction applications.
Potential for new regulatory frameworks and industry standards for uncertainty quantification in AI predictions for public safety.
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Read at arXiv cs.LG