Towards Equitable Wildfire Forecasting for Vulnerable Communities

Climate change is intensifying wildfire risks globally, yet the most devastating impacts are concentrated in underserved regions. While global wildfire forecasting systems are established, there is significant potential to enhance their effectiveness for these vulnerable areas. Currently, many vulnerable regions lack the precise, localized information necessary for effective fire preparedness.

In this study, we employ a novel AI-based model that predicts fire weather index with a lead time of up to 31 days. Our research aims to better understand the intersection of wildfire risks and social vulnerability. We found that our AI-driven approach significantly reduces prediction bias compared to traditional methods derived from the ECMWF. This improvement is most pronounced in the Global South, where the convergence of high poverty and intense wildfire activity makes accurate forecasting essential.

By providing more reliable and actionable data to these underserved regions, our research demonstrates that AI can be a powerful tool for information equity. This study represents a critical step toward ensuring that all nations have access to high-quality tools to manage the escalating risks of climate change.