A Nationwide Benchmark for Wildfire Initial Attack Failure Prediction with Public Environmental Data

2026-06-13

AI Summary

This study proposes WILDFIREIA, the first nationwide benchmark for predicting initial attack failure on wildfires in the United States. Leveraging only publicly available multi-source data at the time of fire detection—including FIRMS/VIIRS thermal anomalies, gridMET meteorological and fire danger indices, LANDFIRE vegetation, fuel, and topography, OpenStreetMap accessibility, and WorldPop population density—the framework rigorously defines event units, labeling rules, temporal splits, and prohibited features to prevent data leakage. Under a unified evaluation protocol, 16 tabular, temporal, spatial, and spatiotemporal models were assessed, with XGBoost achieving the best performance (AUPRC = 53.3%). The analysis reveals that FIRMS/VIIRS data exhibit the lowest redundancy and that fuel type is the strongest static predictor, establishing a foundation for reproducible wildfire risk modeling.

Abstract

Initial attack (IA) is the first wildfire suppression phase, when agencies must quickly decide which fires may escape early control. Existing IA failure prediction studies often use non-public response records or regional settings, so it remains unclear how well public data available at fire discovery time can support IA failure prediction at national scale. We present WILDFIREIA, the first U.S. national-scale benchmark for IA failure prediction from environmental and contextual data available at fire discovery time. WILDFIREIA aligns 38,128 naturally caused FPA-FOD wildfire events with FIRMS/VIIRS thermal detections, gridMET weather and fire-danger variables, LANDFIRE vegetation, fuel, and topography, OpenStreetMap access features, and WorldPop population density. To prevent data leakage, the benchmark fixes the event unit, size-based label rule, chronological split, metrics, and forbidden-feature list, and excludes final fire size, containment timestamps, and post-discovery satellite detections from model inputs. We evaluate 16 representative models across tabular, temporal, spatial, and spatiotemporal families under the same protocol. Results show that public discovery-time data provides useful but incomplete signal for IA failure prediction: XGBoost achieves the best AUPRC of 53.3%; FIRMS/VIIRS is the least redundant source; and fuel is the strongest static predictor when dynamic observations are unavailable. We release preprocessing outputs and model-ready caches to support reproducible research on early wildfire risk assessment: https://github.com/LabRAI/WildfireIA#.

Problem

• wildfire
• initial attack failure
• public environmental data
• national-scale prediction
• early suppression

Innovation

• wildfire initial attack
• national-scale benchmark
• public environmental data
• data leakage prevention
• spatiotemporal modeling