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• W4283164441 abstract "&lt;p&gt;Landslide susceptibility assessment implemented by statistical methods relies on a basic concept according to which past and future landslides depends on the same causes of past failures. As a consequence, statistical inference can explore the relationships between past phenomena and geo-environmental variables to spatially recognize landslide-prone areas. Coherently, the quality and the prediction skill of the model and the relative prediction image heavily depend on the completeness of the landslide scenario exploited. However, landslide archives are frequently biased due to more or less limits we face in reconstructing reliable and complete landslide maps. In this sense, exploiting public available archives compiled from local administration is a goal in landslides susceptibility evaluation. In fact, usually, these types of archives are multitemporal and collect potentially or real destructive phenomena based on direct observation of failures. The use of these types of archives allows obtaining immediately available landslides inventories, saving time and resources from mapping.&lt;/p&gt;&lt;p&gt;On the other hand, public landslide inventory may be wealthy close to urban areas while, usually, failures in the agricultural lands are sporadic or under-represented.&lt;/p&gt;&lt;p&gt;In this study, starting from the available slide (56 cases) and flow (115 cases) inventories of the Torto River basin (420 km&lt;sup&gt;2&lt;/sup&gt;, central-northern Sicily), which were prepared by the &amp;#8220;Dipartimento Regionale dell&amp;#8217;Autorit&amp;#224; di Bacino del Distretto Idrografico Sicilia&amp;#8221;, and a set of twelve geo-environmental predictors, two basin susceptibility models (for slides and flows, respectively) were prepared by applying Multivariate Adaptive Regression Splines (MARS). Good performance for the basin-scale models was then assessed according to cross-validation validation strategies. Then, in a randomly selected small sub-catchment (Sciara stream, 21.5 km&lt;sup&gt;2&lt;/sup&gt;), the prediction images produced at a basin-scale were validated in recalling the landslides of two local systematic and multitemporal remotely recognized inventories. Despite the high performance of the basin-scale models, the results at the local scale showed a poor capacity of the models in detecting the two systematic archives, with a non-acceptable sensitivity (0.67 and 0.57 for slide and flow, respectively). The AUC (Area Under the Curve) values are non-acceptable (0.47 and 0.65 for slide and flow, respectively) also.&lt;/p&gt;&lt;p&gt;In order to &amp;#8220;boost&amp;#8221; the basin-scale susceptibility models, by exploiting a score weighted random selection of 30% of the mapping units of the Sciara stream sub-catchment, a stable/unstable status concerning slide and flow phenomena of the selected cases was assigned by intersecting with the remotely sensed inventories.&lt;/p&gt;&lt;p&gt;Two new basin-scale models were implemented by using these new &amp;#8220;hybrid&amp;#8221; archives. The two prediction images compared with the systematic inventories of the Sciara sub-catchment reveal an increment of the models&amp;#8217; performance, with high accuracy in predicting positive cases both for slide and local flow types. At the same time, persistent precision in detecting stable cases for local flow arises (~0.8), while a decrease in specificity due to an increment of False Positives suggests potentially new future activations for slide phenomena. However, the important increment of the AUC values (from 0.79 to 0.94 and from 0.8 to 0.94, for slide and local flow, respectively) testifies to a general improvement of the main models.&lt;/p&gt;" @default.
• W4283164441 created "2022-06-21" @default.
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• W4283164441 date "2022-06-20" @default.
• W4283164441 modified "2023-09-29" @default.
• W4283164441 title "Incomplete landslide archive in landslide susceptibility assessment: a nested strategy for improving results." @default.
• W4283164441 doi "https://doi.org/10.5194/icg2022-648" @default.
• W4283164441 hasPublicationYear "2022" @default.
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