## 5 Concluding Remarks  
Favorability function modeling was applied to the Tirano South database through five different models of spatial relationships. All the prediction patterns obtained were represented as relative ranks, including their derived target and uncertainty patterns. The 50% lower uncertainty ranks were tentatively used to extract the corresponding target ranks as 50% combination patterns. In them, for all models, the proportion of less uncertain pixels in the rank represents the level of confidence in the prediction. Possibly, the top 10% combination pixels are a significant part of the prediction pattern. We are trying to find answers to questions such as the following: What are the consequences of higher uncertainty for intermediate ranks in combination patterns? How are these diagrams providing a measure of the quality of the prediction? Are they characteristic of the modeling because of the model or the data? How much of the prediction pattern is reliable? How to evaluate the higher ranks in the prediction patterns? Should we select the top 10% or 20% combination ranks? What would our cost/benefit considerations and choices be? All we have generated from the modeling are \"relative\" integrated equal-area ranks that must be  
Figure 10: Comparison of pixel numbers in equal-area target ranks with the pixel numbers in the corresponding low uncertainty target ranks of the 50% combination pattern. Models FZ, LI, LO, LR, and BP are in (**a**–**e**). In (**f**) is the overlay of curves for models FZ and LO. The target equal-area ranks contain about 3230 pixels. Only the higher 160 ranks are shown here.  
interpreted as susceptibility to land-sliding. The model preference needs to be a function of the interpretation of prediction patterns and their representation. In our case, FZ, BP, and LR are equally satisfactory but less so for LI and LO, as to the pattern of predictive performance. However, this may be just one of the criteria that might be used.  
A five-step procedure is proposed for modeling prediction and uncertainty. The uncertainty ranks are considered important to properly select the susceptible part of the study area, i.e., susceptible to the \"next seven\" future\" landslide occurrences. While various other strategies have also been applied for iterative cross validation to obtain slightly different prediction patterns, the sequential exclusion of seven occurrences appears to do justice to the properties of the database. The procedure is proposed as critical in spatial prediction modeling. Independently of the models used, a necessary research issue is in the interpretation of the uncertainty associated with prediction patterns.  
We have described our analysis and modeling results to indicate a way to predict and the assumptions implied. Obviously, we suspect that our experiments on this particular database have a more general significance beyond the specific study area or the five mathematical models used. These considerations, we hope, will be useful to researchers and users of susceptibility maps. This contribution does not provide a solution but poses questions whose answers point at possible solutions.  
Investigation, A.G.F. and A.P. All authors have read and agreed to the published version of the manuscript.  
This contribution was initially and partly supported by the European Commission Project \"Mountain Risks: from Prediction to Management and Governance\" (MRTN-CT-2006-035978, 2007-2010), Mountainrisk (2007).  
The authors are grateful to four anonymous reviewers and to [PERSON] (The Czech Academy of Sciences) for helping to improve this manuscript.  
The authors declare no conflict of interest.