Development of a machine learning system for detection of the atmospheric potential of wildfire-driven thunderstorms

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dc.contributor.authorKrell, Evan
dc.contributor.authorNguyen, Chuyen
dc.contributor.authorNachamkin, Jason
dc.contributor.authorPeterson, David
dc.contributor.authorHyer, Edward
dc.contributor.authorKing, Scott A.
dc.contributor.authorTissot, Philippe
dc.contributor.authorEstrada, Beto
dc.contributor.authorTory, Kevin J.
dc.contributor.authorCampbell, James
dc.date.accessioned2023-09-25T21:23:24Z
dc.date.available2023-09-25T21:23:24Z
dc.date.issued2023-08-30
dc.description.abstractMachine Learning Pipeline: Align pyroCbs to satellite fires > Remove weaker fires > Add engineered features > For valid dataset, drop either fire features or unaligned pyroCbs > K-Best Feature Selection > Data balancing techniques > Train & Evaluate models
dc.identifier.urihttps://hdl.handle.net/1969.6/97385
dc.language.isoen_US
dc.rightsAttribution 4.0 International (CC BY 4.0)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleDevelopment of a machine learning system for detection of the atmospheric potential of wildfire-driven thunderstorms
dc.typePresentation

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