## 5 Conclusions  
On the basis of the obtained results, it is confirmed that grouping landslide inventories from different areas to increase the number of cases can lead to very unreliable results unless further validation tests are carried out. In particular, depending on both the number of landslides and frequency distribution of all the predictors in each of the grouped sectors, the grand model can be seen as having very high performance on average, but is very misleading and unstable on a local scale. In light of this effect, locally calibrated models can have better performance even if trained with a lower number of cases. This would typically lead to attaining a sense of security and considering the obtained prediction image as reliable for the study area, eventually suggesting that the obtained model also be exported to new neighboring unrecognized sectors (e.g., those between the five mapped ones). In this paper, a new approach was adopted, and related tools were proposed for verifying the inventory completeness hypothesis. This approach can be involved in any model building \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline  & **Count** & **Positives** & **Negatives** & **TN** & **FN** & **FP** & **TC** & **Asstivity** & **Specificity** & **AUC** \\ \hline ALL*\_COA & 806,471 & 1895 & 804,576 & 515,857 & 166 & 288,719 & 1729 & 0.64 & 0.91 & 0.64 & 0.85 \\ ALL*\_SSV & 1,429,050 & 382 & 1,428,668 & 1,314,478 & 349 & 114,190 & 33 & 0.92 & 0.09 & 0.92 & 0.61 \\ ALL*\_SVC & 2,794,399 & 4975 & 2,789,424 & 1,813,333 & 1026 & 976,091 & 3949 & 0.65 & 0.79 & 0.65 & 0.79 \\ ALL*\_SMG & 119,964 & 233 & 119,731 & 37,646 & 8 & 82,085 & 225 & 0.32 & 0.97 & 0.31 & 0.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Validation results (confusion matrices) for the ALL* models. procedure so as to obtain warnings about the quality of the source data and its influence on the resolution of the derived susceptibility models. Comparing grand to local models should be a standard procedure when assembling large landslide inventories, even in the case of secondary catchments in large basin-scale studies. The main factors controlling the performance of the grand model are the number of total pixels and the number of positives and the spatial distribution of the predictors. Two main factors hamper the accuracy and reliability of any grand model, based on a presence/absence method: depending on the relative spatial extension of the classes of each covariate, in light of the need to randomly extract the negatives to prepare balanced datasets, using the more diffused classes results in stable conditions; depending on the different levels of completeness of the merged landslide inventories, unstable conditions may come to light in the sectors or catchments with a higher number of mapped landslides. These two effects are much more severe for the categorical variables in the case of inhomogeneous geologic/geomorphologic settings, whilst DTM-derived variables are more unlikely to be so largely different as to mislead the modelling. It is worth noting that the limits produced by the qualitative and quantitative differences in the landslide inventories suggest that the adoption of presence-only methods is not suitable, also in light of the strong influence produced by any unrepresentativeness of the landslide inventories. Optimizing susceptibility models for predicting new debris flow activation sites in volcanic areas is of crucial importance in El Salvador. In fact, under the triggering of the recurrent tropical storms which frequently strike the country, this kind of landslide rapidly evolves along the steep volcano flanks into very destructive debris flow phenomena hitting the hillside areas and causing damage and life losses. Investigating the reliability of prediction images for landslide activation constitutes a mandatory step in obtaining the starting base to be coupled with propagation algorithms for producing complete debris flow event scenarios. Conceptualization, C.M. ([PERSON]), C.M. ([PERSON]), C.C.