## 3 Study Area, Database, and Previous Research
It contained landslide inventories in addition to geological and soil-land use cartographies. For the northern part of the Tirano study area, prediction patterns were thus generated using both active and quiescent translational-rotational landslide scarps, the empirical likelihood ratio function model, and iterative cross-validation strategies based on blind tests [21]. Figure 2: The Tirano South database is shown that will be converted into a direct supporting pattern (DSP) the 70 active landslide trigger areas, **70 at**, and the three categorical and five continuous field digital maps to be converted into indirect supporting patterns (ISPs), **ulp** and **ACDIS**. The explanation is in the text. Indeed, the Tirano study area had become the focus of much more research on landslide susceptibility modeling and other types of landslides and associated floods due to the wealth of information available and certified. [PERSON] et al. (2010a) [22] used two sets of aerial photographic coverages, 20 years apart, to select natural-condition debris flows for the estimation of the spatiotemporal probability of hazard initiation. The resulting distribution map was then used to model runout zone limitations using an empirical GIS-based simulation tool. The experiments indicated that the spatial variability observed needed validation tests for satisfactory interpretation. Furthermore, another study was carried out by those authors [23] of the degree of spatial pattern agreement between different landslide susceptibility maps that showed similar debris-flow predictive power. Their database contained the distribution of 573 scarps, and one half was used for modeling a susceptibility map, while the other half was used to validate the map. The similarity of predictive power, however, did not necessarily produce similar spatial configurations of susceptibility ranks, a fact that pointed at the variability of ranks and related spatial uncertainty. Two more activities in the Tirano study area make it an exemplary instance of supporting background for natural hazard and risk studies. [PERSON] et al. (2010b) [24] constructed a reliable operational inventory of landslides from three available official inventories. Their tasks were (1) to prepare debris flow and factor maps inventories for susceptibility modeling, (2) generate indices of accountability/reliability and selection of factor maps, (3) evaluate/validate susceptibility maps, and (4) compare the results of different maps for combining them into an integrated susceptibility representation. Various methods of subdivision of debris flows and factor maps were obtained, including the separation of the Tirano study area into three more congruous subareas--northern, central, and southern. Again, high spatial variability of the susceptibility ranks was observed as related to the different combinations of factor maps. This made it difficult to delimit the ranks into susceptibility classes. [PERSON] et al. (2012) [25] studied the historical information on landslides and floods in the Tirano area for hazard estimation and definition of tentative risk scenarios. They developed a case study to exemplify the usefulness in their database of 489 records of damaging events (from the years 1600 to 2001), for realistic scenario generation, producing damage classification maps of territorial threats. Recent analyses of the Tirano South database [16] focused on (1) credibility analysis of a fuzzy set modeled prediction pattern of landslide susceptibility and separation of well predicting from poorly predicting landslide occurrences [26] and (2) a generalized procedural strategy for comparisons of prediction patterns of active and dormant landslides by different models [27]. This contribution wants to expand that procedure and strategy attempting to interpret the uncertainties associated with target patterns and their consequences for understanding the prediction patterns generated by the application of those very same models.