Page 1 Application of Neural Networks and Statistical Pattern Recognition Algorithms to Earthquake Risk Evaluation G.Giacinto (*), R.Paolucci ( + ), and F.Roli (*) *Dept. of Electrical and Electronic Eng., University of Cagliari, ITALY Piazza D’Armi, 09123, Cagliari, ITALY e-mail {giacinto, roli}@diee.unica.it + Dept. of Structural Eng., Technical University of Milan, ITALY Piazza Leonardo da Vinci 32, 20133, Milan, ITALY e-mail paolucci@esdra.stru.polimi.it Abstract This paper reports the experimental results on the application of different pattern recognition algorithms to the evaluation of earthquake risk for real geological structures. The study area used for the experiments is related to a well-known geological structure representing a triangular valley over bedrock. Performances obtained by two neural networks and two statistical classifiers are reported and compared. The advantages provided by the use of methods for combining multiple classifiers are also discussed and the related results reported. Keywords: Earthquake risk evaluation; Statistical and neural classifiers; Combination of multiple classifiers. 1. Introduction The ability to realistically predict ground shaking at a given location during an earthquake is crucial for seismic risk prevention strategies in urban systems, as well as for the safe design of major structures. However, the largest seismic events of the last decade have demonstrated that the observed ground shaking can be much more severe than expected and its spatial distribution poorly related to the "earthquake risk maps" previously prepared by seismologists or earthquake engineers (Faccioli, 1996). Therefore, a major improvement of the present ability to compile earthquake risk maps is required to mitigate the impact of earthquakes on urban areas, to plan land use and to prepare effective emergency plans.