An AI-based Digital Twin for Wildfire: Predicting Wildfire Progression and Behavior, and Its Downstream Impacts on Air Quality

Speaker: Mohammad Pourhomayoun, Assistant Professor at California State University

Moderator: Soheil Shayegh, CMCC

Abstract:

Wildfires around the world have become increasingly frequent and severe, posing significant threats to the environment, air quality, and human health. The smoke generated by wildfires contributes to hazardous air quality, exposing people to harmful pollutants that can exacerbate respiratory conditions and lead to long-term health issues. Beyond human impacts, wildfires have devastating effects on ecosystems, causing habitat destruction, loss of biodiversity, and releasing vast amounts of carbon dioxide into the atmosphere, further exacerbating climate change. Despite notable progress in wildfire mitigation technologies in recent years, understanding and predicting wildfire behavior and evolution and real-time adverse effects (such as impacts on air quality) remains highly challenging and complex. 

This research presents an AI-based Digital Twin for Wildfire, delivering an advanced and integrated system designed to enhance the accuracy, efficiency, and real-time responsiveness of wildfire forecasting and management. This wildfire digital twin utilizes a comprehensive set of technologies, including AI-based systems with real-time, high-resolution predictive models to predict wildfire evolution and progression in the hours-to-days ahead as well as its downstream impact on air quality. This system will significantly support firefighters, emergency responders, and various stakeholders in optimizing resource allocation, setting priorities, and executing targeted responses to wildfires.

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