- Application of novel ensemble models to improve landslide susceptibil…
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Application of novel ensemble models to improve landslide susceptibility mapping reliability

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    SYSNO ASEP0573977
    Document TypeJ - Journal Article
    R&D Document TypeJournal Article
    Subsidiary JČlánek ve WOS
    TitleApplication of novel ensemble models to improve landslide susceptibility mapping reliability
    Author(s) Tong, Z. (CN)
    Guan, Q. (CN)
    Arabameri, A. (IR)
    Loche, Marco (USMH-B) ORCID, RID
    Scaringi, G. (CZ)
    Article number309
    Source TitleBulletin of Engineering Geology and the Environment. - : Springer - ISSN 1435-9529
    Roč. 82, č. 8 (2023)
    Number of pages21 s.
    Languageeng - English
    CountryDE - Germany
    KeywordsLandslide susceptibility maps ; Landslide inventory ; Machine learning ; Statistical modeling
    Subject RIVDB - Geology ; Mineralogy
    OECD categoryGeology
    Method of publishingLimited access
    Institutional supportUSMH-B - RVO:67985891
    UT WOS001027857000001
    EID SCOPUS85165221970
    DOI https://doi.org/10.1007/s10064-023-03328-8
    AnnotationMost landslides in the Eastern Golestan province in Iran occur in the Doji watershed. Their number, however, lies at the lower limit for reliable statistical analyses. By selecting a statistical sample in an area with rather homogeneous conditions (thereby reducing the number of meaningful covariates), significant insights can nevertheless be obtained. We relied on an inventory of 145 landslides which discerns between types of movement and implemented six machine learning algorithms (Decorate, DE-REPTree, Random Subspace, RS-REPTree, Dagging, and DA-REPTree) to produce landslide susceptibility maps. This allowed us to evaluate the relative importance and the effect of covariates in the models and identify factors that are consistently associated with the presence of landslides. Our results demonstrate that, even for a small landslide inventory, reliable susceptibility maps can be produced for homogeneous landscapes. We discuss that our approach could be used to assess the reliability of statistical approaches at small scales, where a distinctive trigger is lacking.
    WorkplaceInstitute of Rock Structure and Mechanics
    ContactIva Švihálková, svihalkova@irsm.cas.cz, Tel.: 266 009 216
    Year of Publishing2024
    Electronic addresshttps://doi.org/10.1007/s10064-023-03328-8
Number of the records: 1  

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