Territorially-Specialized Machine Learning Models for Wildfire Risk Prediction Across Argentina Using Satellite Data and H3 Hexagonal Grids

Wildfire risk prediction in large, ecologically diverse countries requires models that account for regional variation in fire drivers. We present GeoAlertAR-ML, a wildfire risk prediction system for Argentina that uses an ensemble of regionally specialized Random Forest classifiers operating over a national hexagonal grid of 13,231 H3 cells. Unlike global fire danger indices or single-model approaches, our system trains independent models for each of five ecological regions (Centro, Cuyo, NEA, NOA, Patagonia), routed by a K-Nearest Neighbors classifier with 99.96% accuracy. The system ingests multisource satellite data — including GFS meteorological fields, GSMaP precipitation, MODIS vegetation indices, SRTM topography, Dynamic World land cover, and GHSL population density — and produces daily hexagon-level risk predictions in four categories (Low, Moderate, High, Extreme). Cross-validated F1-score averaged 93.2% across regions using GroupKFold to prevent spatial data leakage. Operational validation against NASA FIRMS hotspots confirmed 100% detection of active fire clusters in zones predicted as High or Extreme risk, including the February 2026 Comarca Andina crisis (280+ FIRMS hotspots, 68–72% predicted risk). A complementary 7-day forecasting model (Model B) based on regional XGBoost classifiers trained on GFS hindcast data achieved 77.8% F1-score with minimal degradation across forecast horizons (0.3–3.4% from day+1 to day+7). Additionally, the system identifies potential anthropogenic ignition anomalies by flagging discrepancies between low predicted meteorological risk and observed fire activity. GeoAlertAR-ML demonstrates that regionally specialized models, trained on territory-specific fire histories, outperform generic global approaches for national-scale wildfire risk assessment. The system has been operational since late 2025 and won the NASA Space Apps Challenge 2025 Best Mission Concept award. Keywords: wildfire prediction, machine learning, Random Forest, XGBoost, satellite remote sensing, H3 hexagonal grid, Argentina, regional models, FIRMS validation