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Lessons learnt from field tests in some potentially unstable slopes in Switzerland
Sarah Springman, Armin Askarinejad, Francesca Casini, Sven Friedel, Peter Kinzler, Philipp Teysseire, Andrea Thielen, 2012, original scientific article

Abstract: Rain-induced slope instability is a significant natural hazard in Switzerland, Slovenia and elsewhere in Europe. This contribution was prepared especially for the 12th Šuklje Symposium, and recognises that landslides occur both in mountain regions as well as in lowland regions during and following extreme-rainfall conditions. The Institute (and Professorship) for Geotechnical Engineering at the Swiss Federal Institute of Technology (ETH Zürich) has been engaged over several years in projects concerned with the characterisation, monitoring and modelling behaviour of slopes in mainly granular porous media across the full range of altitudes in Switzerland. A link is made to the doyen of the Šuklje day and then three case histories are presented and discussed to demonstrate the principal reactions to seasonal rainfall. A small slip was released in two of these cases and the “triggering” factors have been investigated and are discussed in this contribution. It transpires that the mode of inslope drainage influences the way in which the ground saturates and hence the volume of the potentially unstable ground. Simple stability analyses using limit equilibrium and soil parameters that have been amended to account for unsaturated soil behaviour were found to function well for slopes in largely granular media.
Keywords: rain-induced landslides, slope stability, case histories, monitoring, characterisation, modelling
Published in DKUM: 13.06.2018; Views: 581; Downloads: 50
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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: 592; Downloads: 48
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