Clustering and classification in hazard evaluation

This study is aimed at characterising the attenuation of earthquakes in Italy by exploiting the information provided by the macroseismic fields of the DBMI04 database. The analysis was carried out for the most damaging earthquakes (epicentral intensity of at least VII), which were subdivided into a learning set, composed of earthquakes with a considerable number of macroseismic data points, and a classification set, composed of earthquakes with less rich macroseismic information. The learning set was partitioned into classes of events with similar macroseismic behaviour using agglomerative hierachical clustering; the good quality of the earthquakes of the learning set guaranteed sharp partitions into these classes. Then, the remaining events were assigned to the classes obtained through recursive partitioning. The probability distribution of the intensity at sites for each class was chosen to be Binomial, and the unknown parameters were estimated via the Bayesian method. The models obtained can be used to forecast damage scenarios of future earthquakes.