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dc.creatorKrušić, Jelka
dc.creatorMarjanović, Miloš
dc.creatorSamardžić-Petrović, Mileva
dc.creatorAbolmasov, Biljana
dc.creatorAndrejev, Katarina
dc.creatorMiladinović, Aleksandar
dc.date.accessioned2020-05-01T18:37:43Z
dc.date.available2020-05-01T18:37:43Z
dc.date.issued2017
dc.identifier.urihttp://grafar.grf.bg.ac.rs/handle/123456789/1936
dc.description.abstractLandslide Susceptibility Assessment is becoming a very productive re-search area, wherein different modeling approaches are practiced to delineate zones of the high-low likelihood of landslide occurrence. However, there is no strong consensus on which approach is the most adequate. The reason behind the lack of the general view on the performance of different approaches could be partially explained by the particularity of each study. To evaluate the effi-ciency of different approaches they need to be applied under the same conditions for the same study area. Herein, we examined three different approaches, in-cluding expert, deterministic and Machine Learning, on the example of Ljubo-vija Municipality in western Serbia. The study area has been known as suscep-tible to landslides, and represents good ground for assessing the chosen methods. It is represented by complex geology, prone to landslides that are commonly hosted in thick weathering crust of Paleozoic formations, composed of schists and meta-sediments. Under extreme triggering conditions, such as the one that unfolded in May 2014, these thick weathering crusts saturate, and give way to a variety of landslide and flash-flood processes that we will be focusing on in this study. The application of the expert-approach, through Analytical Hierarchy Process provided a rough assessment map. The deterministic model, which couples simple infinite slope and hydrological model, provided us with lower quality results, when compared to the expert-based one. This could be explained by the assumptions used in the model are too simplistic to generically model a wide range of landslide typology. Finally, Machine Learning approach, using the Random Forest algorithm, provided significantly better results and showed that it can cope with versatile landslide typology over larger scales. Its AUC performance is about 0.75 which is considerably outperforming the AUC values of the other two models, which were up to 0.55, i.e. at the level of random guessen
dc.language.isoensr
dc.relationTR36009sr
dc.rightsopenAccesssr
dc.sourceGeofizikasr
dc.subjectlandslide susceptibilitysr
dc.subjectAnalytical Hierarchy Processsr
dc.subjectdeterministicsr
dc.subjectMachine Learningsr
dc.subjectRandom Forestsr
dc.titleComparison of expert, deterministic and Machine Learning approach for landslide susceptibility assessment in Ljubovija Municipality, Serbiaen
dc.typearticlesr
dc.rights.licenseARRsr
dc.citation.volume34
dc.identifier.doi10.15233/gfz.2017.34.15
dc.identifier.doi10.15233/gfz.2017.34.15
dc.identifier.fulltexthttp://grafar.grf.bg.ac.rs/bitstream/id/7406/34-2_Krusic_et_al.pdf
dc.identifier.scopus2-s2.0-85044964043
dc.identifier.wos000425726300003
dc.type.versionpublishedVersionsr


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