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Title:Landslide assessment of the Strača basin (Croatia) using machine learning algorithms
Authors:Marjanović, Miloš (Author)
Kovačević, Miloš (Author)
Bajat, Branislav (Author)
Mihalić Arbanas, Snježana (Author)
Abolmasov, Biljana (Author)
Fakulteta za gradbeništvo, prometno inženirstvo in arhitekturo Univerze v Mariboru (Authorship owner)
Files:.pdf Acta_geotechnica_Slovenica_2011_Marjanovic_et_al._Landslide_assessment_of_the_Straca_basin_(Croatia)_using_machine_learning_algorithms.pdf (382,76 KB)
 
URL http://fgserver3.fg.um.si/journal-ags/2011-2/article-3.asp
 
Language:English
Work type:Scientific work (r2)
Typology:1.01 - Original Scientific Article
Organization:FGPA - Faculty of Civil Engineering, Transportation Engineering and Architecture
Abstract:In this research, machine learning algorithms were compared in a landslide-susceptibility assessment. Given the input set of GIS layers for the Starča Basin, which included geological, hydrogeological, morphometric, and environmental data, a classification task was performed to classify the grid cells to: (i) landslide and non-landslide cases, (ii) different landslide types (dormant and abandoned, stabilized and suspended, reactivated). After finding the optimal parameters, C4.5 decision trees and Support Vector Machines were compared using kappa statistics. The obtained results showed that classifiers were able to distinguish between the different landslide types better than between the landslide and non-landslide instances. In addition, the Support Vector Machines classifier performed slightly better than the C4.5 in all the experiments. Promising results were achieved when classifying the grid cells into different landslide types using 20% of all the available landslide data for the model creation, reaching kappa values of about 0.65 for both algorithms.
Keywords:landslides, support vector machines, decision trees classifier, Starča Basin
Year of publishing:2011
Number of pages:str. 45-55
Numbering:št. 2, Letn. 8
ISSN:1854-0171
UDC:550.348.435(497.5)
ISSN on article:1854-0171
COBISS_ID:262545920 Link is opened in a new window
NUK URN:URN:SI:UM:DK:IV0E4EOX
Copyright:Fakulteta za gradbeništvo, prometno inženirstvo in arhitekturo Univerze v Mariboru
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Record is a part of a journal

Title:Acta geotechnica Slovenica
Shortened title:Acta geotech. Slov.
Publisher:Fakulteta za gradbeništvo, prometno inženirstvo in arhitekturo Univerze v Mariboru
ISSN:1854-0171
COBISS.SI-ID:215987712 New window

Secondary language

Language:Slovenian
Title:Bočna nosilnost kratkih togih pilotov v dvoslojnih nevezljivih tleh
Abstract:V tej raziskavi so avtorji primerjali algoritme strojnega učenja v okviru prognoze drsenja terena. Na osnovi GIS slojev področja kotline Starča, ki so vključevali geološke, hidrogeološke, morfometrijske in druge prostorske podatke, je napravljena klasifikacija mrežnih celic na (i) primerih »drsečega« in »stabilnega terena«, (ii) različnih tipih drsečega terena (»potencialen-neaktiven«, »stabiliziran-saniran« in »reaktiviran«). Po optimizaciji parametrov modela za C4.5 decision trees in Support Vector Machines so primerjali dobljene rezultate klasifikacije s pomočjo kappa statistike. Rezultati kažejo, da sta omenjena modela bolje razlikovala med različnimi tipi drsečega terena kot med drsečim in stabilnim terenom. Prav tako je bil klasifikator Support Vector Machines v vseh preizkusih nekoliko uspešnejši od C4.5. Spodbudne rezultate so dobili v eksperimentu, kjer so klasificirali različne tipe drsečega terena, uporabili pa so samo 20% od skupnega števila podatkov o drsečem terenu. V tem primeru so za oba klasifikatorja dobili vrednost kappa okoli 0.65.
Keywords:geofizika, plazovi, modeli, algoritmi, kotlina Strača, Hrvaška


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  1. Acta geotechnica Slovenica

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