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Landslide Assessment of the Starča Basin (Croatia) Using Machine-Learning Algorithms (CROSBI ID 177509)

Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija

Marjanović, Miloš ; Kovačević, Miloš ; Bajat, Branislav ; Mihalić, Snježana ; Abolmasov, Biljana Landslide Assessment of the Starča Basin (Croatia) Using Machine-Learning Algorithms // Acta Geotechnica Slovenica, 2011 (2011), 2; 45-55

Podaci o odgovornosti

Marjanović, Miloš ; Kovačević, Miloš ; Bajat, Branislav ; Mihalić, Snježana ; Abolmasov, Biljana

engleski

Landslide Assessment of the Starča Basin (Croatia) Using Machine-Learning Algorithms

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.

landslides; support vector machines; decision trees classifier; Starča Basin

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Podaci o izdanju

2011 (2)

2011.

45-55

objavljeno

1854-0171

Povezanost rada

Rudarstvo, nafta i geološko inženjerstvo

Indeksiranost