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Wildfire is one of the biggest disasters that frequently occurs on the westcoast of the United States. Many efforts have been made to understand thecauses of the increases in wildfire intensity and frequency in recent years. Inthis work, we propose static and dynamic prediction models to analyze andassess the areas with high wildfire risks in California by utilizing amultitude of environmental data including population density, NormalizedDifference Vegetation Index (NDVI), Palmer Drought Severity Index (PDSI), treemortality area, tree mortality number, and altitude. Moreover, we focus on abetter understanding of the impacts of different factors so as to informpreventive actions. To validate our models and findings, we divide the land ofCalifornia into 4,242 grids of 0.1 degrees $\times$ 0.1 degrees in latitude andlongitude, and compute the risk of each grid based on spatial and temporalconditions. To verify the generalizability of our models, we further expand thescope of wildfire risk assessment from California to Washington without anyfine tuning. By performing counterfactual analysis, we uncover the effects ofseveral possible methods on reducing the number of high risk wildfires. Takentogether, our study has the potential to estimate, monitor, and reduce therisks of wildfires across diverse areas provided that such environment data isavailable.

From Static to Dynamic Prediction: Wildfire Risk Assessment Based on Multiple Environmental Factors