Title: | Landslide assessment of the Strača basin (Croatia) using machine learning algorithms |
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Authors: | ID Marjanović, Miloš (Author) ID Kovačević, Miloš (Author) ID Bajat, Branislav (Author) ID Mihalić Arbanas, Snježana (Author) ID Abolmasov, Biljana (Author) ID Fakulteta za gradbeništvo, prometno inženirstvo in arhitekturo Univerze v Mariboru (Authorship owner) |
Files: | Acta_geotechnica_Slovenica_2011_Marjanovic_et_al._Landslide_assessment_of_the_Straca_basin_(Croatia)_using_machine_learning_algorithms.pdf (382,76 KB) MD5: 703813029563B01BA05C03EEFCA4E844
http://fgserver3.fg.um.si/journal-ags/2011-2/article-3.asp
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Language: | English |
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Work type: | Scientific work (r2) |
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Typology: | 1.01 - Original Scientific Article |
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Organization: | FGPA - Faculty of Civil Engineering, Transportation Engineering and Architecture
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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. |
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Keywords: | landslides, support vector machines, decision trees classifier, Starča Basin |
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Year of publishing: | 2011 |
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Publication status in journal: | Published |
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Article version: | Publisher's version of article |
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Number of pages: | str. 45-55 |
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Numbering: | Letn. 8, št. 2 |
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ISSN: | 1854-0171 |
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UDC: | 550.348.435(497.5) |
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ISSN on article: | 1854-0171 |
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COBISS.SI-ID: | 262545920  |
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NUK URN: | URN:SI:UM:DK:IV0E4EOX |
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Copyright: | Fakulteta za gradbeništvo, prometno inženirstvo in arhitekturo Univerze v Mariboru |
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Publication date in DKUM: | 13.06.2018 |
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Views: | 619 |
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Downloads: | 48 |
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Metadata: |  |
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Categories: | Misc.
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