Landslide susceptibility mapping based on data mining models in Lesser Caucasus and Kura foreland basin (Armenia and Azerbaijan)
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17310%2F25%3AA2603D1S" target="_blank" >RIV/61988987:17310/25:A2603D1S - isvavai.cz</a>
Result on the web
<a href="https://www.tandfonline.com/doi/full/10.1080/19475705.2025.2537221" target="_blank" >https://www.tandfonline.com/doi/full/10.1080/19475705.2025.2537221</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1080/19475705.2025.2537221" target="_blank" >10.1080/19475705.2025.2537221</a>
Alternative languages
Result language
angličtina
Original language name
Landslide susceptibility mapping based on data mining models in Lesser Caucasus and Kura foreland basin (Armenia and Azerbaijan)
Original language description
Landslides are a major geological hazard causing significant loss of life and infrastructure damage worldwide. Landslide susceptibility mapping is a crucial, though developing, tool for understanding the spatial distribution of landslide hazard. This study addresses the absence of a comprehensive landslide inventory, limited understandingof causative factors and the lack of regional-scale susceptibility maps for the LesserCaucasus and Kura Basin (LC-KB). A landslide inventory was created for the LesserCaucasus of Azerbaijan and compiled with other inventories, documenting 3,659 landslidepolygons. Sixteen causative factors were analysed, and multicollinearity tests confirmedno significant correlations. Three Machine Learning (ML) models—LogisticRegression (LGR), Support Vector Machine (SVM) and Extreme Gradient Boosting(XGBoost)—were fine-tuned to create landslide susceptibility maps. Slope is consistentlythe most influential factor across all models. Results suggest stronger influenceof seismic factors than climatic ones. XGBoost achieves the highest accuracy (0.81) onthe testing data set, followed by SVM (0.80) and LGR (0.73). The first two modelsshow strong validation performance, with AUC values of 0.89 and 0.87, respectively,while LGR shows a lower AUC of 0.78. The results are vital for planning and disastermanagement, highlighting areas needing urgent mitigation.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10508 - Physical geography
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Geomatics, Natural Hazards and Risk
ISSN
1947-5705
e-ISSN
1947-5713
Volume of the periodical
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Issue of the periodical within the volume
1
Country of publishing house
GB - UNITED KINGDOM
Number of pages
27
Pages from-to
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UT code for WoS article
001550860600001
EID of the result in the Scopus database
2-s2.0-105013463104