Inference of Wildfire Causes From Their Physical, Biological, Social and Management Attributes
Yavar Pourmohamad, John T. Abatzoglou, Erica Fleishman, Karen C. Short, J. K. Shuman, Amir AghaKouchak, Matthew A. Williamson, Seyd Teymoor Seydi, Mojtaba Sadegh
Abstract
Abstract Effective wildfire prevention includes actions to deliberately target different wildfire causes. However, the cause of an increasing number of wildfires is unknown, hindering targeted prevention efforts. We developed a machine learning model of wildfire ignition cause across the western United States on the basis of physical, biological, social, and management attributes associated with wildfires. Trained on wildfires from 1992 to 2020 with 12 known causes, the overall accuracy of our model exceeded 70% when applied to out‐of‐sample test data. Our model more accurately separated wildfires ignited by natural versus human causes (93% accuracy), and discriminated among the 11 classes of human‐ignited wildfires with 55% accuracy. Our model attributed the greatest percentage of 150,247 wildfires from 1992 to 2020 for which the ignition source was unknown to equipment and vehicle use (21%), lightning (20%), and arson and incendiarism (18%).