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  • 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 (en)
Title
  • Landslide susceptibility assessment with machine learning algorithms
  • Landslide susceptibility assessment with machine learning algorithms (en)
skos:prefLabel
  • Landslide susceptibility assessment with machine learning algorithms
  • Landslide susceptibility assessment with machine learning algorithms (en)
skos:notation
  • RIV/61989592:15310/09:00010834!RIV10-MSM-15310___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GA205/09/1079), S
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
  • Marjanovič, Miloš
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 323167
http://linked.open...ai/riv/idVysledku
  • RIV/61989592:15310/09:00010834
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Gora Mountain; Landslide susceptibility; algorithms (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [B3A0377195B6]
http://linked.open...i/riv/mistoVydani
  • New York
http://linked.open...i/riv/nazevZdroje
  • International Conference on Intelligent Networking and Collaborative Systems INCoS 2009
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Marjanovič, Miloš
number of pages
http://purl.org/ne...btex#hasPublisher
  • IEEE Computer Society Press
https://schema.org/isbn
  • 978-0-7695-3858-7
http://localhost/t...ganizacniJednotka
  • 15310
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