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

• URL: http://arxiv.org/abs/2606.15529v1
• Date: Sun, 14 Jun 2026 01:03:13 GMT
• Title: A Nationwide Benchmark for Wildfire Initial Attack Failure Prediction with Public Environmental Data
• Abstract summary: We present the first U.S. national-scale benchmark for initial attack (IA) failure prediction from environmental and contextual data available at fire discovery time.<n> 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.<n>Results show that public discovery-time data provides useful but incomplete signal for IA failure prediction.
• Score: 62.031815260604056
• License: http://creativecommons.org/licenses/by/4.0/
• 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#.

Related papers

• Does Your Wildfire Prediction Model Actually Work, or Just Score Well? [17.463683393779647]
  Existing Earth foundation models (Earth FMs) are pretrained for general atmospheric and geophysical objectives rather than wildfire forecasting.<n>We introduce WILDFIRE-FM, the first foundation model pretrained specifically for wildfire prediction using weather, active-fire observations, topography, vegetation, and static environmental data.<n>We introduce a fixed-contract evaluation framework with two controlled checks: a fixed-output check for matching-rule effects and a fixed-feature check for head-selection effects.
  arXiv  Detail & Related papers  (2026-05-14T04:28:10Z)
• Set Prediction for Next-Day Active Fire Forecasting [8.116506215900882]
  Next-day active fire forecasts can support early warning, disaster response, forest risk assessment, and downstream estimation of fire-related carbon emissions.<n>We propose Wildfire Ignition Set Predictor (WISP), a query-based model that reformulates next-day active fire forecasting as point-set prediction.
  arXiv  Detail & Related papers  (2026-05-11T09:56:17Z)
• Predicting the Containment Time of California Wildfires Using Machine Learning [0.0]
  We built machine learning models to predict the number of days it will require to fully contain a wildfire in California.<n>This study compared the performance of baseline ensemble regressor, Random Forest and XGBoost, with a Long Short-Term Memory (LSTM) neural network.
  arXiv  Detail & Related papers  (2025-12-10T17:14:20Z)
• Rethinking Metrics and Benchmarks of Video Anomaly Detection [58.37571339811799]
  Video Anomaly Detection (VAD) aims to detect anomalies that deviate from expectation.<n>Existing VAD metrics are influenced by single annotation bias.<n>Existing benchmarks lack the capability to evaluate scene overfitting of fully/weakly-supervised algorithms.
  arXiv  Detail & Related papers  (2025-05-25T08:09:42Z)
• Generating Fine-Grained Causality in Climate Time Series Data for Forecasting and Anomaly Detection [67.40407388422514]
  We design a conceptual fine-grained causal model named TBN Granger Causality. Second, we propose an end-to-end deep generative model called TacSas, which discovers TBN Granger Causality in a generative manner. We test TacSas on climate benchmark ERA5 for climate forecasting and the extreme weather benchmark of NOAA for extreme weather alerts.
  arXiv  Detail & Related papers  (2024-08-08T06:47:21Z)
• FreDF: Learning to Forecast in the Frequency Domain [54.2091536822376]
  Time series modeling presents unique challenges due to autocorrelation in both historical data and future sequences.<n>We propose the Frequency-enhanced Direct Forecast (FreDF) which mitigates label autocorrelation by learning to forecast in the frequency domain.
  arXiv  Detail & Related papers  (2024-02-04T08:23:41Z)
• Generative Algorithms for Fusion of Physics-Based Wildfire Spread Models with Satellite Data for Initializing Wildfire Forecasts [0.0]
  Recent progress in using satellites to detect fire locations provides the opportunity to use measurements to improve fire spread forecasts. This work develops a method for inferring the history of a wildfire from satellite measurements.
  arXiv  Detail & Related papers  (2023-09-05T23:24:34Z)
• Image-Based Fire Detection in Industrial Environments with YOLOv4 [53.180678723280145]
  This work looks into the potential of AI to detect and recognize fires and reduce detection time using object detection on an image stream. To our end, we collected and labeled appropriate data from several public sources, which have been used to train and evaluate several models based on the popular YOLOv4 object detector.
  arXiv  Detail & Related papers  (2022-12-09T11:32:36Z)
• Multi-time Predictions of Wildfire Grid Map using Remote Sensing Local Data [0.0]
  This paper proposes a distributed learning framework that shares local data collected in ten locations in the western USA throughout local agents. The proposed model has distinct features that address the characteristic need in prediction evaluations, including dynamic online estimation and time-series modeling.
  arXiv  Detail & Related papers  (2022-09-15T22:34:06Z)
• Complex Event Forecasting with Prediction Suffix Trees: Extended Technical Report [70.7321040534471]
  Complex Event Recognition (CER) systems have become popular in the past two decades due to their ability to "instantly" detect patterns on real-time streams of events. There is a lack of methods for forecasting when a pattern might occur before such an occurrence is actually detected by a CER engine. We present a formal framework that attempts to address the issue of Complex Event Forecasting.
  arXiv  Detail & Related papers  (2021-09-01T09:52:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.