Machine Learning Model Doubles Accuracy of Global Landslide ‘Nowcasts’

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dc.contributor.authorSmith, Esprit
dc.date.accessioned2022-10-06T14:20:59Z
dc.date.available2022-10-06T14:20:59Z
dc.date.issued2021-06-10
dc.description.urihttps://www.nasa.gov/feature/esnt/2021/machine-learning-model-doubles-accuracy-of-global-landslide-nowcastsen_US
dc.format.extent5 pagesen_US
dc.genrearticlesen_US
dc.identifierdoi:10.13016/m2jxmb-qwo6
dc.identifier.citation“Machine Learning Model Doubles Accuracy of Global Landslide ‘Nowcasts’”, NASA’s Earth Science News Team, Greenbelt, MD (June 10, 2021). https://www.nasa.gov/feature/esnt/2021/machine-learning-model-doubles-accuracy-of-global-landslide-nowcastsen_US
dc.identifier.urihttp://hdl.handle.net/11603/26103
dc.language.isoen_USen_US
dc.publisherNASAen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC GESTAR II Collection
dc.relation.ispartofAbout UMBC and Its People
dc.rightsThis is a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.en_US
dc.rightsPublic Domain Mark 1.0*
dc.rights.urihttp://creativecommons.org/publicdomain/mark/1.0/*
dc.subject Thomas Stanleyen_US
dc.subjectBenefits to Youen_US
dc.subjectEarth, Goddard Space Flight Centeren_US
dc.subjectGPM (Global Precipitation Measurement)en_US
dc.subjectHazardsen_US
dc.subjectJet Propulsion Laboratoryen_US
dc.subjectLanden_US
dc.subjectSMAP (Soil Moisture Active Passive)en_US
dc.titleMachine Learning Model Doubles Accuracy of Global Landslide ‘Nowcasts’en_US
dc.typeTexten_US

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