  Wildfire is one of the biggest disasters that frequently occurs on the west
coast of the United States. Many efforts have been made to understand the
causes of the increases in wildfire intensity and frequency in recent years. In
this work, we propose static and dynamic prediction models to analyze and
assess the areas with high wildfire risks in California by utilizing a
multitude of environmental data including population density, Normalized
Difference Vegetation Index (NDVI), Palmer Drought Severity Index (PDSI), tree
mortality area, tree mortality number, and altitude. Moreover, we focus on a
better understanding of the impacts of different factors so as to inform
preventive actions. To validate our models and findings, we divide the land of
California into 4,242 grids of 0.1 degrees $\times$ 0.1 degrees in latitude and
longitude, and compute the risk of each grid based on spatial and temporal
conditions. By performing counterfactual analysis, we uncover the effects of
several possible methods on reducing the number of high risk wildfires. Taken
together, our study has the potential to estimate, monitor, and reduce the
risks of wildfires across diverse areas provided that such environment data is
available.
