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Shallow-landslide spatial structure interpretation using a multi-geophysical approach
Aleksandar Ristić, Biljana Abolmasov, Miro Govedarica, Dušan Petrovački, Aleksandra Ristić, 2012, original scientific article

Abstract: We present an methodology for a more detailed and less ambiguous spatial structure interpretation of small, shallow landslides. The spatial structure interpretation of this type of landslides bases on both underground and surface models and requires high-density data. This methodology involves the use of ground-penetrating radar (GPR), electrical resistivity tomography (ERT) and terrestrial laser scanning (TLS) techniques. GPR technique, used for the definition of the underground structure model, provides a time-efficient survey that yields high-resolution data, making it suitable for a shallow subsurface analysis. ERT technique was used only to confirm the results obtained by the GPR survey, since it is more time consuming and more convenient for larger and deeper landslides investigations. The surface model is created using TLS technique, which is time- and cost-effective, produces a large amount of data and is favourable for smaller areas, such as the analysed type of landslides. To the best of the authors’ knowledge, existing procedures based on either conventional or non-invasive geophysical methods, observe, almost exclusively, larger and deeper landslides. Their real-time monitoring involves a number of sensors and is hardly applicable to small landslides because of their number, location and dimensions. Considering the benefits of each applied technique and the interpretation of the results obtained from field data, it is clear that the main advantages of the realized application are the efficiency and applicability for small shallow landslides whose number and impact on the environment are dominant. Therefore, it represents a solid basis for landslide mitigation. The verification of the methodology was made on a small, shallow landslide in the village of Vinča, near Belgrade, Serbia.
Keywords: shallow landslide, GPR, ERT, TLS
Published in DKUM: 13.06.2018; Views: 597; Downloads: 43
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Landslide assessment of the Strača basin (Croatia) using machine learning algorithms
Miloš Marjanović, Miloš Kovačević, Branislav Bajat, Snježana Mihalić Arbanas, Biljana Abolmasov, 2011, original scientific article

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
Published in DKUM: 13.06.2018; Views: 593; Downloads: 48
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