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Using the location data of landslides and information on geo-environmental conditions, the landslide susceptibility in Poland was calculated using statistical methods. It shows that 15% of the country's area is susceptible to landslides. The greatest threats occur in the Carpathians and in many regions of the Sudetes, uplands and in the riverbanks. The most vulnerable areas are the slopes in the range of 9-30°, which are built by the flysch sediments in the faulted zones. The areas susceptible to landslides include over a million buildings and about 7,000 km of roads.

This paper presents the results of analysis of landslides distribution in the Sudetes (SW Poland). Our study was based on the analysis of the LiDAR-data digital elevation model and integrated with investigations of different factors for landslide development. The results of the study showed relationships between the spatial distribution of landslides and geology of their basement. For the areas built by Permo-Mesozoic and late Variscan sedimentary and volcanogenic rocks, the tectonic and lithological factors are predominant for landslide occurrences. The largest landslides have a tectonic affinity and represent a frontal type of geometry. The relationships between geological conditions and mass movements were also confirmed by the constructed landslide susceptibility map of the Sudetes.

Let us consider a general population R. Each object belonging to the population R is characterized by a pair of correlated random vectors (& I). Both X and _Y may be mixtures of discrete and continuous random variables. It will be assumed that our population R consists of k groups nl, ..., 3zk, which depend on the value of the random vector 1. A certain object, which is an element of one of the k groups ni, ..., nk, has to be classified into the correct group. The knowledge of the value of the random vector would permit its correct classification, but the observation of this vector is difficult or dangerous and we must assign the individual on the basis of the observation of the random vector X. The classification procedure is based on randomized decision function 6* which minimizes the risk function i.e. Bayes decision function. We give also two empirical Bayes classification rules i.e. decision functions based on the sample from popula+ion n and having property that their risks converge t o Bayes risk when the sample size increases.

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