Predicting large wildfires in the Contiguous United States using deep neural networks

Abstract

Over the last several decades, large wildfires have become increasingly common across the United States causing a disproportionate impact on forest health and function, human well-being, and the economy. Here, we examine the severity of large wildfires across the Contiguous United States over the past decade (2011 to 2020) using a wide array of meteorological, land cover, and topographical features in a deep neural network model. A total of 4538 wildfire incidents were used in the analysis covering 87,305 square miles of burned area. We observed the highest number of large wildfires in California, Texas, and Idaho, with lightning causing 43% of these incidents. Importantly, results indicate that the severity of wildfire occurrences is highly correlated with the weather, land cover, and elevation of the study area as indicated from their SHapley Additive exPlanations values. Overall, different variants of data-driven models and their results could provide useful guidance in managing landscapes for large wildfires under changing climate and disturbance regimes.