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Title of Talk:
Wildfire Engineering: Integrating Models and Data to Provide Engineered Solutions For Wildfire Management 

Abstract:

Wildland fires play a vital role in maintaining healthy ecosystems, but the growing wildland-urban interface, coupled with climate change and other human activities, has significantly increased wildfire hazards in recent decades. Wildfires are now among the most critical natural threats to our physical, environmental, social, economic, and health systems. This presentation highlights recent advancements in active-fire decision support and pre-fire risk assessment.

For active-fire decision support, we focus on the need for low-latency, high-resolution data to enhance situational awareness, and fire behavior modeling to predict wildfire dynamics faster than real-time. We will introduce key concepts of wildland fire modeling, with emphasis on our recent contributions in fuel characterization, canopy fuel modeling, and fire spotting. Given the challenges of collecting observational data during wildland fires, we will also present a deep learning method that improves satellite observation resolution, facilitating refined fire monitoring.

Additionally, a probabilistic wildfire risk assessment framework will be discussed, to quantify risk as the probability measure of loss from wildfires. Finally, we will address current technical challenges in wildfire monitoring, simulation, and risk assessment, while outlining a forward-looking vision for wildfire engineering research to encourage further contributions from the engineering community.

Short Bio:

Dr. Hamed Ebrahimian is an Assistant Professor in the Department of Civil and Environmental Engineering at the University of Nevada, Reno (UNR), and the recipient of the 2024 Nevada Jason Geddes Rising Researcher Award. He joined UNR in 2019 after transitioning from a senior engineering role in industry, preceded by a postdoctoral appointment at the California Institute of Technology (Caltech). He earned his Ph.D. in Structural Engineering in 2015 and a second master’s degree in Mechanical Engineering in 2013, both from UC San Diego. Prior to his doctoral studies, Dr. Ebrahimian worked for several years as a structural design engineer in his home country.

Dr. Ebrahimian’s research lies at the intersection of computational methods and data science, focusing on prediction, monitoring, data assimilation, and uncertainty quantification. He is currently leading a multi-institutional, multidisciplinary research project in wildfire engineering and risk assessment. Drawing from his background in earthquake engineering, he applies his expertise in computational modeling and data-driven approaches to the wildfire engineering research. His additional research interests include digital twinning of civil and energy infrastructure for monitoring, virtual sensing, and damage diagnosis and prognosis.