A benchmark dataset and workflow for landslide susceptibility zonation

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985891%3A_____%2F24%3A00599857" target="_blank" >RIV/67985891:_____/24:00599857 - isvavai.cz</a>
Alternative codes found
RIV/00216208:11310/24:10486353
Result on the web
<a href="https://doi.org/10.1016/j.earscirev.2024.104927" target="_blank" >https://doi.org/10.1016/j.earscirev.2024.104927</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.earscirev.2024.104927" target="_blank" >10.1016/j.earscirev.2024.104927</a>

Result language
angličtina
Original language name
A benchmark dataset and workflow for landslide susceptibility zonation
Original language description
Landslide susceptibility shows the spatial likelihood of landslide occurrence in a specific geographical area and is a relevant tool for mitigating the impact of landslides worldwide. As such, it is the subject of countless scientific studies. Many methods exist for generating a susceptibility map, mostly falling under the definition of statistical or machine learning. These models try to solve a classification problem: given a collection of spatial variables, and their combination associated with landslide presence or absence, a model should be trained, tested to reproduce the target outcome, and eventually applied to unseen data.nContrary to many fields of science that use machine learning for specific tasks, no reference data exist to assess the performance of a given method for landslide susceptibility. Here, we propose a benchmark dataset consistingnof 7360 slope units encompassing an area of about 4, 100 km2 in Central Italy. Using the dataset, we tried to answer two open questions in landslide research: (1) what effect does the human variability have in creating susceptibility models,(2) how can we develop a reproducible workflow for allowing meaningful model comparisons within the landslide susceptibility research community.
Czech name
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Czech description
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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
10505 - Geology

Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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