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
dc.format.extent5 pagesen
dc.genrearticlesen
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
dc.identifier.urihttp://hdl.handle.net/11603/26103
dc.language.isoenen
dc.publisherNASAen
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC GESTAR II Collection
dc.relation.ispartofAbout UMBC and Its People
dc.rightsPublic Domain Mark 1.0*
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
dc.rights.urihttp://creativecommons.org/publicdomain/mark/1.0/*
dc.subjectThomas Stanleyen
dc.subjectBenefits to Youen
dc.subjectEarth, Goddard Space Flight Centeren
dc.subjectGPM (Global Precipitation Measurement)en
dc.subjectHazardsen
dc.subjectJet Propulsion Laboratoryen
dc.subjectLanden
dc.subjectSMAP (Soil Moisture Active Passive)en
dc.titleMachine Learning Model Doubles Accuracy of Global Landslide ‘Nowcasts’en
dc.typeTexten

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