4.6 Article

Using Public Landslide Inventories for Landslide Susceptibility Assessment at the Basin Scale: Application to the Torto River Basin (Central-Northern Sicily, Italy)

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APPLIED SCIENCES-BASEL
卷 13, 期 16, 页码 -

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MDPI
DOI: 10.3390/app13169449

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landslide susceptibility; public landslide inventory; MARS; landslide incompleteness

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The quality of landslide inventories used for calibration greatly affects the quality of the model and its prediction image in statistical landslide susceptibility evaluation. This research proposes a two-step susceptibility modeling procedure to verify and solve the limitations caused by the incompleteness and mapping inaccuracy of regional-scale inventories. By applying this procedure to the Torto River basin in Italy, it was found that the limitations of the initial models were largely solved by the recalibrated models, suggesting the proposed procedure as a suitable modeling strategy for regional susceptibility studies.
In statistical landslide susceptibility evaluation, the quality of the model and its prediction image heavily depends on the quality of the landslide inventories used for calibration. However, regional-scale inventories made available by public territorial administrations are typically affected by an unknown grade of incompleteness and mapping inaccuracy. In this research, a procedure is proposed for verifying and solving such limits by applying a two-step susceptibility modeling procedure. In the Torto River basin (central-northern Sicily, Italy), using an available regional landslide inventory (267 slide and 78 flow cases), two SUFRA_1 models were first prepared and used to assign a landslide susceptibility level to each slope unit (SLU) in which the study area was partitioned. For each of the four susceptibility classes that were obtained, 30% of the mapping units were randomly selected and their stable/unstable status was checked by remote analysis. The new, increased inventories were finally used to recalibrate two SUFRA_2 models. The prediction skills of the SUFRA_1 and SUFRA_2 models were then compared by testing their accuracy in matching landslide distribution in a test sub-basin where a high-resolution systematic inventory had been prepared. According to the results, the strong limits of the SUFRA_1 models (sensitivity: 0.67 and 0.57 for slide and flow, respectively) were largely solved by the SUFRA_2 model (sensitivity: 1 for both slide and flow), suggesting the proposed procedure as a possibly suitable modeling strategy for regional susceptibility studies.

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