TY  - JOUR
AU  - Marjanović, Miloš
AU  - Kovačević, Miloš
AU  - Bajat, Branislav
AU  - Mihalić, Snježana
AU  - Abolmasov, Biljana
PY  - 2011
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/396
AB  - In this research, machine learning algorithms were compared in a landslide-susceptibility assessment. Given the input set of GIS layers for the Starca 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.
T2  - Acta Geotechnica Slovenica
T1  - Landslide assessment of the Starca basin (Croatia) using machine learning algorithms
EP  - 55
IS  - 2
SP  - 45
VL  - 8
UR  - https://hdl.handle.net/21.15107/rcub_grafar_396
ER  - 

@article{
author = "Marjanović, Miloš and Kovačević, Miloš and Bajat, Branislav and Mihalić, Snježana and Abolmasov, Biljana",
year = "2011",
abstract = "In this research, machine learning algorithms were compared in a landslide-susceptibility assessment. Given the input set of GIS layers for the Starca 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.",
journal = "Acta Geotechnica Slovenica",
title = "Landslide assessment of the Starca basin (Croatia) using machine learning algorithms",
pages = "55-45",
number = "2",
volume = "8",
url = "https://hdl.handle.net/21.15107/rcub_grafar_396"
}

Marjanović, M., Kovačević, M., Bajat, B., Mihalić, S.,& Abolmasov, B.. (2011). Landslide assessment of the Starca basin (Croatia) using machine learning algorithms. in Acta Geotechnica Slovenica, 8(2), 45-55.
https://hdl.handle.net/21.15107/rcub_grafar_396

Marjanović M, Kovačević M, Bajat B, Mihalić S, Abolmasov B. Landslide assessment of the Starca basin (Croatia) using machine learning algorithms. in Acta Geotechnica Slovenica. 2011;8(2):45-55.
https://hdl.handle.net/21.15107/rcub_grafar_396 .

Marjanović, Miloš, Kovačević, Miloš, Bajat, Branislav, Mihalić, Snježana, Abolmasov, Biljana, "Landslide assessment of the Starca basin (Croatia) using machine learning algorithms" in Acta Geotechnica Slovenica, 8, no. 2 (2011):45-55,
https://hdl.handle.net/21.15107/rcub_grafar_396 .