Landslide susceptibility assessment using SVM machine learning algorithm

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F11%3A10225214" target="_blank" >RIV/61989592:15310/11:10225214 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1016/j.enggeo.2011.09.006" target="_blank" >http://dx.doi.org/10.1016/j.enggeo.2011.09.006</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.enggeo.2011.09.006" target="_blank" >10.1016/j.enggeo.2011.09.006</a>

Result language
angličtina
Original language name
Landslide susceptibility assessment using SVM machine learning algorithm
Original language description
The main objective of this research is to examine the possibility of automating the process of landslide susceptibility mapping by using machine learning techniques. The desired automated procedure assumes that after the initial acquisition of the necessary spatial data, an expert is presented with a (possibly small) representative region of the whole terrain. Such a scenario assumes a supervised learning approach in which the expert performs mapping in the representative region and the map subsequentlyproduced is used for training the machine which would perform the mapping task in the rest of the area.
Czech name
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Czech description
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Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
DE - Earth magnetism, geodesy, geography
OECD FORD branch
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Project
<a href="/en/project/GA205%2F09%2F1079" target="_blank" >GA205/09/1079: Methods of Artificial Inteligence in GIS</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2011
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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