Uncertainty-Aware Deep Learning for Wildfire Danger Forecasting

2025-09-29

AI Summary

Existing deep learning models for wildfire hazard prediction lack uncertainty quantification, limiting their reliability and operational deployment. This paper proposes an uncertainty-aware deep learning framework for 1–10-day wildfire risk forecasting. Methodologically, it jointly models epistemic (model) and aleatoric (data) uncertainties to capture their complementary characteristics; further, it optimizes a joint objective combining F1-score maximization and expected calibration error (ECE) minimization to enhance both predictive performance and probabilistic calibration. Innovatively, the framework generates hierarchical risk maps annotated with uncertainty estimates and enables automatic filtering of low-confidence predictions. Experiments demonstrate a 2.3% improvement in next-day F1 score and a 2.1% reduction in ECE. Moreover, the temporal evolution of predicted uncertainty aligns closely with real-world environmental dynamics, significantly strengthening decision-support capabilities.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Uncertainty Representations
• Multiagent Systems: Multiagent Systems under Uncertainty

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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 captures epistemic (model) and aleatoric (data) uncertainty to enhance short-term wildfire danger forecasting. In the next-day forecasting, our best-performing model improves the F1 Score by 2.3% and reduces the Expected Calibration Error by 2.1% compared to a deterministic baseline, enhancing both predictive skill and calibration. Our experiments confirm the reliability of the uncertainty estimates and illustrate their practical utility for decision support, including the identification of uncertainty thresholds for rejecting low-confidence predictions and the generation of well-calibrated wildfire danger maps with accompanying uncertainty layers. Extending the forecast horizon up to ten days, we observe that aleatoric uncertainty increases with time, showing greater variability in environmental conditions, while epistemic uncertainty remains stable. Finally, we show that although the two uncertainty types may be redundant in low-uncertainty cases, they provide complementary insights under more challenging conditions, underscoring the value of their joint modeling for robust wildfire danger prediction. In summary, our approach significantly improves the accuracy and reliability of wildfire danger forecasting, advancing the development of trustworthy wildfire DL systems.

Problem

• Enhancing wildfire danger forecasting accuracy and reliability using deep learning
• Addressing uncertainty quantification limitations in current wildfire prediction models
• Jointly modeling epistemic and aleatoric uncertainty for robust decision support

Innovation

• Uncertainty-aware deep learning framework for wildfire forecasting
• Jointly captures epistemic and aleatoric uncertainty types
• Generates calibrated danger maps with uncertainty layers