Open Session - SWISS GEOSCIENCE MEETINGs

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Symposium 9: Natural Hazards and Risks
This hypothesis can be verified on several landslide data sets such as rock falls, shallow landslides, snow avalanches, etc. In
any case, this permits to unify all the different approaches taking into account the difference between energy line and
Farböschung.
REFERENCES
Corominas J. 1996: The angle of reach as mobility index for small and large landslides. Can Geotech J 33:260–271.
Heim A. 1932: Bergsturz und Menschenleben - Fretz und Wasmuth, Zurich, 218 pp.
Evans, S., Hungr, O., 1993: The assessment of rockfall hazard at the base of talus slopes. Canadian Geotechnical Journal, 30,
620-636.
Toppe, R. 1987: Terrain models: a tool for natural hazard mapping. In: Avalanche formation, movement and effects. Edited
by B. Salm &H. Gubler. International Association of Hydrological Sciences. Wallingford, UK. Publication 162, pp. 629-638.
297, 269-281.
.11
Machine learning algorithms for spatial data. Case studies:
environmental pollution, natural hazards, renewable resources
Kanevski Mikhail*, Pozdnoukhov Alexei*, Timonin Vadim*
*Institute of Geomatics and Analysis of Risk (IGAR), University of Lausanne, CH-1015 (Mikhail.Kanevski@unil.ch)
This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms.
Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the
machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental
phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning
algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern
recognition, regression/mapping and probability density modelling.
In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of
different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of
geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when
problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are
usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models
concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments
in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds.
An important part of the study deals with the analysis of relevant variables and models’ inputs. This problem is approached
by using different feature selection/feature extraction nonlinear tools.
To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil
mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis
(avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality
of spaces considered varies from 2 to more than 30.
Figures 1, 2, 3 demonstrate some results of the studies and their outputs.
Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.
Acknowledgements. The study was partially supported by the Swiss National Science Foundation projects GeoKernels (project
No 200021-113944) and Clusterville (project No 100012-113506).
REFERENCES
Kanevski, M. (Editor), 2008. Advanced Mapping of Environmental Data. ISTE Ltd., John Wiley & Sons Inc, 313 pp.