Wildfire Danger

Wildfire danger prediction aims to accurately forecast the likelihood and extent of wildfires, crucial for resource allocation and mitigation efforts. Current research heavily utilizes deep learning, employing convolutional neural networks (CNNs), graph neural networks (GNNs), and recurrent neural networks (like ConvLSTMs) to analyze diverse datasets encompassing weather patterns, vegetation, and human activity, often incorporating transfer learning and causal inference techniques to improve accuracy and interpretability. These advanced modeling approaches offer the potential for significantly improved spatial and temporal resolution in wildfire risk assessments, leading to more effective prevention and response strategies.