Predictive Understanding of Wildfire Ignitions Across the Western United States
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| Wydane w: | Earth's Future vol. 14, no. 1 (Jan 1, 2026) |
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| 1. autor: | |
| Kolejni autorzy: | , , , , , , , |
| Wydane: |
John Wiley & Sons, Inc.
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| Hasła przedmiotowe: | |
| Dostęp online: | Citation/Abstract Full Text Full Text - PDF |
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| 022 | |a 2328-4277 | ||
| 024 | 7 | |a 10.1029/2025EF006935 |2 doi | |
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| 045 | 0 | |b d20260101 | |
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| 100 | 1 | |a Pourmohamad, Yavar |u Department of Civil Engineering, Boise State University, Boise, ID, USA | |
| 245 | 1 | |a Predictive Understanding of Wildfire Ignitions Across the Western United States | |
| 260 | |b John Wiley & Sons, Inc. |c Jan 1, 2026 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Wildfires have increasingly affected human and natural systems across the western United States (WUS) in recent decades. Given that the majority of ignitions are human‐caused and potentially preventable, improving the ability to predict fire occurrence is critical for effective wildfire prevention and risk mitigation. We used over 500,000 wildfire ignition records from 2000 to 2020 to develop machine learning models that predict daily ignition probability across the WUS and incorporate a wide range of physical, biological, social, and administrative variables. A key innovation of this work is development of novel sampling techniques for representing ignition absence. Unlike traditional purely random sampling or hyper‐sampling, which does not account for temporally autocorrelated factors (such as droughts, insect outbreaks, and heatwaves) and spatially autocorrelated factors (such as proximity to human settlements, infrastructure presence, and fuel type), we introduce spatially and temporally stratified sampling of ignition absence. By drawing absence samples near the location and time of historical ignitions, we better captured the complex environmental and anthropogenic conditions associated with fire occurrence or lack thereof. Models trained without stratified sampling produced ignition probability maps that consistently overestimated fire risk during high fire danger periods, whereas models incorporating stratified fire absence samples more accurately captured the spatial and temporal variability of fire potential and achieved predictive accuracies exceeding 95%. In addition to operational utility for fire prevention and resource allocation, our approach offers insights into the drivers of wildfire ignitions and highlights the value of incorporating spatial and temporal structure in absence sampling for wildfire modeling. | |
| 651 | 4 | |a United States--US | |
| 653 | |a Resource allocation | ||
| 653 | |a Temporal variability | ||
| 653 | |a Fire hazards | ||
| 653 | |a Models | ||
| 653 | |a Topography | ||
| 653 | |a Heat waves | ||
| 653 | |a Drought | ||
| 653 | |a Population density | ||
| 653 | |a Winter | ||
| 653 | |a Insects | ||
| 653 | |a Machine learning | ||
| 653 | |a Time series | ||
| 653 | |a Fire prevention | ||
| 653 | |a Random sampling | ||
| 653 | |a Autocorrelation | ||
| 653 | |a Sampling techniques | ||
| 653 | |a Human settlements | ||
| 653 | |a Wildfires | ||
| 653 | |a Vegetation | ||
| 653 | |a Fire danger | ||
| 653 | |a Sampling methods | ||
| 653 | |a Statistical sampling | ||
| 653 | |a Ignition | ||
| 653 | |a Probability | ||
| 653 | |a Pest outbreaks | ||
| 653 | |a Forest & brush fires | ||
| 653 | |a Land use planning | ||
| 653 | |a Risk reduction | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Abatzoglou, John T. |u Management of Complex Systems Department, University of California, Merced, CA, USA | |
| 700 | 1 | |a Fleishman, Erica |u College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis, OR, USA | |
| 700 | 1 | |a Belval, Erin |u USDA Forest Service, Rocky Mountain Research Station, Fort Collins, CO, USA | |
| 700 | 1 | |a Short, Karen C. |u USDA Forest Service, Rocky Mountain Research Station, Missoula, MT, USA | |
| 700 | 1 | |a Williamson, Matthew |u Human‐Environment Systems, Boise State University, Boise, ID, USA | |
| 700 | 1 | |a Perlmutter, Michael |u Department of Mathematics, Boise State University, Boise, ID, USA | |
| 700 | 1 | |a Seydi, Seyd Teymoor |u Department of Civil Engineering, Boise State University, Boise, ID, USA | |
| 700 | 1 | |a Sadegh, Mojtaba |u Department of Civil Engineering, Boise State University, Boise, ID, USA | |
| 773 | 0 | |t Earth's Future |g vol. 14, no. 1 (Jan 1, 2026) | |
| 786 | 0 | |d ProQuest |t Publicly Available Content Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3289956618/abstract/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3289956618/fulltext/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3289956618/fulltextPDF/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |