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Statements

Subject Item
n2:RIV%2F61989592%3A15310%2F09%3A00010834%21RIV10-MSM-15310___
rdf:type
skos:Concept n19:Vysledek
dcterms:description
Case study addresses NW slopes of Fruška Gora Mountain, Serbia. Landslide activity is quite notorious in this region, especially along the Danube's right river bank, and recently intensified seismicity coupled with atmospheric precipitation might be critical for triggering new landslide occurrences. Hence, it is not a moment too soon for serious landslide susceptibility assessment in this region. State-of-the-art approaches had been taken into consideration, cutting down to the Support Vector Machine (SVM) and k-Nearest Neighbor (k-NN) algorithms, trained upon expert based model of landslide susceptibility (a multi-criteria analysis). The latter involved Analytical Hierarchy Process (AHP) for weighting influences of different input parameters. These included elevation, slope angle, aspect, distance from flows, vegetation cover, lithology, and rainfall, to represent the natural factors of the slope stability. Processed in a GIS environment (as discrete or float raster layers) trough AHP, those par Case study addresses NW slopes of Fruška Gora Mountain, Serbia. Landslide activity is quite notorious in this region, especially along the Danube's right river bank, and recently intensified seismicity coupled with atmospheric precipitation might be critical for triggering new landslide occurrences. Hence, it is not a moment too soon for serious landslide susceptibility assessment in this region. State-of-the-art approaches had been taken into consideration, cutting down to the Support Vector Machine (SVM) and k-Nearest Neighbor (k-NN) algorithms, trained upon expert based model of landslide susceptibility (a multi-criteria analysis). The latter involved Analytical Hierarchy Process (AHP) for weighting influences of different input parameters. These included elevation, slope angle, aspect, distance from flows, vegetation cover, lithology, and rainfall, to represent the natural factors of the slope stability. Processed in a GIS environment (as discrete or float raster layers) trough AHP, those par
dcterms:title
Landslide susceptibility assessment with machine learning algorithms Landslide susceptibility assessment with machine learning algorithms
skos:prefLabel
Landslide susceptibility assessment with machine learning algorithms Landslide susceptibility assessment with machine learning algorithms
skos:notation
RIV/61989592:15310/09:00010834!RIV10-MSM-15310___
n3:aktivita
n18:S n18:P
n3:aktivity
P(GA205/09/1079), S
n3:dodaniDat
n11:2010
n3:domaciTvurceVysledku
Marjanovič, Miloš
n3:druhVysledku
n14:D
n3:duvernostUdaju
n10:S
n3:entitaPredkladatele
n6:predkladatel
n3:idSjednocenehoVysledku
323167
n3:idVysledku
RIV/61989592:15310/09:00010834
n3:jazykVysledku
n8:eng
n3:klicovaSlova
Gora Mountain; Landslide susceptibility; algorithms
n3:klicoveSlovo
n4:algorithms n4:Gora%20Mountain n4:Landslide%20susceptibility
n3:kontrolniKodProRIV
[B3A0377195B6]
n3:mistoVydani
New York
n3:nazevZdroje
International Conference on Intelligent Networking and Collaborative Systems INCoS 2009
n3:obor
n12:DE
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
1
n3:projekt
n15:GA205%2F09%2F1079
n3:rokUplatneniVysledku
n11:2009
n3:tvurceVysledku
Marjanovič, Miloš
s:numberOfPages
421
n16:hasPublisher
IEEE Computer Society Press
n7:isbn
978-0-7695-3858-7
n17:organizacniJednotka
15310