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GIS-based machine learning models for assessing landslide impact in King County, Washington, USA

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Published in:Progress in Earth and Planetary Science
Format: Online Article RSS Article
Published: 2026
Subjects:
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container_title Progress in Earth and Planetary Science
description
discipline_display Geography
discipline_facet Geography
format Online Article
RSS Article
genre Journal Article
id rss_article:70766
institution FRELIP
journal_source_facet Progress in Earth and Planetary Science
publishDate 2026
publishDateSort 2026
record_format rss_article
spellingShingle GIS-based machine learning models for assessing landslide impact in King County, Washington, USA
Geography
General
Geography
sub_discipline_display General
sub_discipline_facet General
subject_display Geography
General
Geography
Geography
General
Geography
subject_facet Geography
General
Geography
title GIS-based machine learning models for assessing landslide impact in King County, Washington, USA
title_auth GIS-based machine learning models for assessing landslide impact in King County, Washington, USA
title_full GIS-based machine learning models for assessing landslide impact in King County, Washington, USA
title_fullStr GIS-based machine learning models for assessing landslide impact in King County, Washington, USA
title_full_unstemmed GIS-based machine learning models for assessing landslide impact in King County, Washington, USA
title_short GIS-based machine learning models for assessing landslide impact in King County, Washington, USA
title_sort gis-based machine learning models for assessing landslide impact in king county, washington, usa
topic Geography
General
Geography
url https://link.springer.com/article/10.1186/s40645-026-00817-8