Comprehensive Exam - Yavar Pourmohamad

Yavar Pourmohamad
Computing PhD, Data Science emphasis

Center Plaza 368 or pre-register to attend via Zoom

Physical, Social, and Biological Attributes for Improved Understanding and Prediction of Wildfires: FPA FOD- 2 Attributes Dataset

Abstract: Wildfires are increasingly impacting social and environmental systems in the United States. The ability to mitigate the adverse effects of wildfires increases with understanding of the social, physical, and biological conditions that co-occurred with or caused the wildfire ignitions and contributed to the wildfire impacts. To this end, we developed the FPA FOD-Attributes dataset, which augments the sixth version of the Fire Program Analysis-Fire Occurrence Database (FPA FOD v6) with nearly 270 attributes that coincide with the date and location of each wildfire ignition in the United States. FPA FOD v6 contains information on location, jurisdiction, discovery time, cause, and final size of >2.3 million wildfires from 1992-2020 in the United States. For each wildfire, we added physical (e.g., weather, climate, topography, infrastructure), biological (e.g., land cover, normalized difference vegetation index), social (e.g., population density, social vulnerability index), and administrative (e.g., national and regional preparedness level, jurisdiction) attributes. This publicly available dataset can be used to answer numerous questions about the covariates associated with human- and lightning-caused wildfires. Furthermore, the FPA FOD-Attributes dataset can support descriptive, diagnostic, predictive, and prescriptive wildfire analytics, including development of machine learning models.

Committee: Dr. Mojtaba Sadegh (Advisor and Chair), Dr. Matt Williamson, Dr. Edoardo Serra, Dr. Arvin Farid (Comprehensive Exam External Evaluator)