  Over the past decade, the number of wildfire has increased significantly
around the world, especially in the State of California. The high-level
concentration of greenhouse gas (GHG) emitted by wildfires aggravates global
warming that further increases the risk of more fires. Therefore, an accurate
prediction of wildfire occurrence greatly helps in preventing large-scale and
long-lasting wildfires and reducing the consequent GHG emissions. Various
methods have been explored for wildfire risk prediction. However, the complex
correlations among a lot of natural and human factors and wildfire ignition
make the prediction task very challenging. In this paper, we develop a deep
learning based data augmentation approach for wildfire risk prediction. We
build a dataset consisting of diverse features responsible for fire ignition
and utilize a conditional tabular generative adversarial network to explore the
underlying patterns between the target value of risk levels and all involved
features. For fair and comprehensive comparisons, we compare our proposed
scheme with five other baseline methods where the former outperformed most of
them. To corroborate the robustness, we have also tested the performance of our
method with another dataset that also resulted in better efficiency. By
adopting the proposed method, we can take preventive strategies of wildfire
mitigation to reduce global GHG emissions.
