The thesis deals with the problem of the prediction of landslide movements with artificial neural networks (ANN). At the beginning of the thesis the landslides are introduced in general and especially the Macesnik landslide. Later on geodetic methods for observing movements of landslides are described from referential geodetic methods to geodetic methods for mass collection. The list of content of the project is proposed, which should be used for geodetic observations of the Macesnik slide. The description of all past geodetic observations of the Macesnik slide is given and analysis of the effect of rainfall on the landslide movements is presented. In the fifth part artificial neural networks are presented, the training principles of artificial neural networks and a detailed explanation of artificial neural network with error back propagation algorithm. In the experimental part we have presented the use of artificial neural networks for the prediction of landslide movements. It is shown that the use of ANN can be a successful alternative to other methods, which require thorough geological, hydrological and geomechanical evaluation. It is usual for landslides that none of influential factors alone can reliably explain the sliding mechanism of the slope. Also, with statistical tools it is difficult to correctly determine dependency between individual influential factors. To use artificial neural networks we do not need to know all influential factors and we also d
