Data-Driven Fire-Zone Segmentation for Improved Short-Term Wildfire Prediction
Researchers propose an unsupervised fire-zone segmentation algorithm to improve wildfire prediction accuracy by moving beyond uniform grid discretization.
Current wildfire models often use arbitrary grid discretization, which fails to account for the heterogeneous nature of ignition sites. This study introduces a method combining watershed detection and K-means clustering to define prediction units based on historical fire data, demonstrating that data discretization strategy significantly impacts model performance.