ABSTRACT
Within the frame of risk analysis the knowledge of the existence and location of hazard zones is an essential prerequisite. Besides the well-known deterministic and statistical methods, the use of artificial neural nets is a new and very promising approach for automated hazard analysis. The advantage provided by neural nets is their ability to handle non-linearities as well as model and parameter uncertainties. Several different types of nets have been developed for landslide recognition and different training methods have been tested. One interesting approach is to use neural nets not only for the main task, the landslide recognition, but also to solve individually smaller problems or tasks associated to the main problem. Therefore neural nets for scarp recognition were developed, whose output is used as an additional input for the nets for landslide recognition. In contrast to teams of neural nets, where the nets work parallel, the nets are switched in line here. It is of particular interest that the neural nets can identify different types of mass movements with one and the same net and also classify the runout-area in one step as a hazard zone. In tests carried out to identify landslides in test areas in the Eastern Alps, the best nets have classified up to 86% of the areas correctly.
