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.
Balson, Joshua; Chinchilla, Matt; Lu, Cam; Washburn, Jeff; and Lohia, Nibhrat
"Identification and Characterization of Forest Fire Risk Zones Leveraging Machine Learning Methods,"
SMU Data Science Review: Vol. 5:
2, Article 3.
Available at: https://scholar.smu.edu/datasciencereview/vol5/iss2/3
Applied Statistics Commons, Categorical Data Analysis Commons, Climate Commons, Data Science Commons, Environmental Monitoring Commons, Forest Management Commons, Natural Resources and Conservation Commons, Natural Resources Management and Policy Commons, Other Forestry and Forest Sciences Commons, Probability Commons, Statistical Models Commons