  The reliable statistical characterization of the spatial and temporal
properties of large earthquakes occurrence is one of the most debated issues in
seismic hazard assessment, due to the unavoidably limited observations from
past events. We show that pattern recognition techniques, which are designed in
a formal and testable way, may provide significant space-time constraints about
impending strong earthquakes. This information, when combined with physically
sound methods for ground shaking computation, like the neo-deterministic
approach (NDSHA), may produce effectively preventive seismic hazard maps.
Pattern recognition analysis of morphostructural data provide quantitative and
systematic criteria for identifying the areas prone to the largest events,
taking into account a wide set of possible geophysical and geological data,
whilst the formal identification of precursory seismicity patterns (by means of
CN and M8S algorithms), duly validated by prospective testing, provides useful
constraints about impending strong earthquakes at the intermediate space-time
scale. According to a multi-scale approach, the information about the areas
where a strong earthquake is likely to occur can be effectively integrated with
different observations (e.g. geodetic and satellite data), including regional
scale modeling of the stress field variations and of the seismic ground
shaking, so as to identify a set of priority areas for detailed investigations
of short-term precursors at local scale and for microzonation studies. Results
from the pattern recognition of earthquake prone areas (M>=5.0) in the Po plain
(Northern Italy), as well as from prospective testing and validation of the
time-dependent NDSHA scenarios are presented.
