Data-Driven Wildfire Propagation Modeling with FARSITE-EnKF

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dc.contributor.advisorTrouve, Arnauden_US
dc.contributor.authorTheodori, Maria Fayeen_US
dc.contributor.departmentFire Protection Engineeringen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2016-09-07T05:30:33Z
dc.date.available2016-09-07T05:30:33Z
dc.date.issued2016en_US
dc.description.abstractThe goal of this study is to provide a framework for future researchers to understand and use the FARSITE wildfire-forecasting model with data assimilation. Current wildfire models lack the ability to provide accurate prediction of fire front position faster than real-time. When FARSITE is coupled with a recursive ensemble filter, the data assimilation forecast method improves. The scope includes an explanation of the standalone FARSITE application, technical details on FARSITE integration with a parallel program coupler called OpenPALM, and a model demonstration of the FARSITE-Ensemble Kalman Filter software using the FireFlux I experiment by Craig Clements. The results show that the fire front forecast is improved with the proposed data-driven methodology than with the standalone FARSITE model.en_US
dc.identifierhttps://doi.org/10.13016/M2BN4T
dc.identifier.urihttp://hdl.handle.net/1903/18625
dc.language.isoenen_US
dc.subject.pqcontrolledEngineeringen_US
dc.subject.pqcontrolledComputer scienceen_US
dc.subject.pquncontrolleddata assimilationen_US
dc.subject.pquncontrolledforecasten_US
dc.subject.pquncontrolledmodelingen_US
dc.subject.pquncontrolledwildfireen_US
dc.titleData-Driven Wildfire Propagation Modeling with FARSITE-EnKFen_US
dc.typeThesisen_US

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