###### Abstract  
This research is focused on the evaluation of the reliability of regional landslide susceptibility models obtained by exploiting inhomogeneous (for quality, resolution and/or triggering related type and intensity) collected inventories for calibration. At a large-scale glance, merging more inventories can result in well-performing models hiding potential strong predictive deficiencies. An example of the limits that such kinds of models can display is given by a landslide susceptibility study, which was carried out for a large sector of the coastal area of El Salvador, where an apparently well-performing regional model (AUC = 0.87) was obtained by regressing a dataset through multivariate adaptive regression splines (MARS), including five landslide inventories from volcanic areas (Ibompago and Coatepeque caldera; San Salvador, San Miguel, and San Vicente Volcano). A multiscale validation strategy was applied to verify its actual predictive skill on a local base, bringing to light the loss in the predictive power of the regional model, with a lowering of AUC (20% on average) and strong effects in terms of sensitivity and specificity. incomplete landslide archives; MARS; Central America; validation procedures; regional-scale; debris flows +
Footnote †: journal: _Sciences_  
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hurricanes frequently result in water saturation and neutral emission increasing in the regolithic mantle, causing the triggering of slope failures [5; 6; 7]. As a consequence, the volcanoes and the caldera's inner flanks are very frequently affected by landslides of a debris slide/flow type. These failures, in light of the high steepness of the initiation zones, very frequently take the form of very fast and long-runout debris flow phenomena, threatening those villages, which are set along the track channels or at the foot of the slopes. In recent years, the Nepaja (2020) [8] and San Vicente (2009) [9; 10] disasters clearly illustrated this kind of phenomenon, resulting in widespread damage to houses and high numbers of injured and dead. Differing from rockfall susceptibility studies [11; 12; 13], slide- and flow-type landslides are typically analyzed on a basin to regional scale, meaning that large inventories are required for robust modelling. In particular, a need arises to detect the potential initiation sites of landslides. To this end, landslide susceptibility modelling based on statistical analysis can offer a suitable approach for obtaining quantitative, objective, and validated prediction images of the potential triggering sites, which can then be processed with propagation tracking algorithms for a full hazard assessment. Indeed, civil protection urgently requires regional-scale landslide susceptibility scenarios attempting to define statistically based national maps, eventually even exploiting limited but available landslide inventories for their calibration. To this end, grouping multiple clustered available datasets is frequently adopted as a solution to obtain landslide inventories large enough to train the statistical models. However, such landslide datasets can result in heterogeneity in terms of spatial distribution, the expertise of the operators, classification and mapping criteria, survey recognition methods and resolution (field, remote, reports), epoch and related triggering events, etc. It is worth noting that these limits could hamper the resolution and precision of the predictive models without giving clear effects down from standard validation procedures. A number of landslide susceptibility studies have been conducted in the last fifteen years. In particular, post-Hurricane Mitch (1998) and post the 2001 earthquake, landslide inventories were processed through principal component analysis for assessing landslide susceptibility of an area in the extreme north-western sector of the country [14].