Assessing the Impact of Human Inhibition Factors on Wildfire Risk Prediction using Deep Learning

Wildfires are becoming increasingly frequent and destructive, creating significant environmental, economic, and public health challenges. While modern machine learning models can predict wildfire risk, they often overlook factors that reduce fire severity, such as firefighting infrastructure and fuel management programs. In this project, I developed deep learning models that incorporate these human inhibition factors into wildfire forecasting across California. The results demonstrate that accounting for human intervention can improve predictive accuracy and provide new insights into strategies for reducing wildfire risk.

• Environmental Science
• Artificial Intelligence