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Full-Text Articles in Forest Management
Identification And Characterization Of Forest Fire Risk Zones Leveraging Machine Learning Methods, Joshua Balson, Matt Chinchilla, Cam Lu, Jeff Washburn, Nibhrat Lohia
Identification And Characterization Of Forest Fire Risk Zones Leveraging Machine Learning Methods, Joshua Balson, Matt Chinchilla, Cam Lu, Jeff Washburn, Nibhrat Lohia
SMU Data Science Review
Across the United States, record numbers of wildfires are observed costing billions of dollars in property damage, polluting the environment, and putting lives at risk. The ability of emergency management professionals, city planners, and private entities such as insurance companies to determine if an area is at higher risk of a fire breaking out has never been greater. This paper proposes a novel methodology for identifying and characterizing zones with increased risks of forest fires. Methods involving machine learning techniques use the widely available and recorded data, thus making it possible to implement the tool quickly.
Slides: Summary: Sources Of Stress And The Changing Context Of Natural Resources Law And Policy In The New West, William R. Travis
Slides: Summary: Sources Of Stress And The Changing Context Of Natural Resources Law And Policy In The New West, William R. Travis
The Future of Natural Resources Law and Policy (Summer Conference, June 6-8)
Presenter: Dr. William R. Travis, Department of Geography, University of Colorado at Boulder
43 slides