4.7 Article

Susceptibility zoning of shallow landslides in fine grained soils by statistical methods

Journal

CATENA
Volume 139, Issue -, Pages 250-264

Publisher

ELSEVIER
DOI: 10.1016/j.catena.2015.12.017

Keywords

Landslide; Susceptibility; Zoning; Statistical analysis; Terrain zoning units

Funding

  1. Project of National Relevance PRIN Landslide risk mitigation through sustainable countermeasures [CUP D41J12000460001]
  2. Italian Ministry of Education (MIUR)

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The paper proposes a methodology, in two successive steps, to zone the susceptibility to shallow landslides in fine grained soils by means of statistical methods. The first step of the methodology, aiming at defining, calibrating and validating the statistical analysis, ends with a landslide susceptibility computational map; the second step of the methodology is employed to produce a susceptibility map for zoning purposes. This structured methodology arises from the need to distinguish, at any given scale of analysis, between the spatial discretization needed to perform the statistical computations (terrain computational units) and terrain units useful for zoning purposes (terrain zoning units). The applicability of the proposed methodology is tested, at two different scales (1:25,000 and 1:5000), in two areas of southern Italy, the test area for the larger scale being a portion of the test area used for the analysis at the smaller scale. This allows for the generalization of the obtained results through the comparison, for the same phenomena in the same geo-environmental context, of the predisposing factors at two scales of analysis. In both analyses, the relevant variables for the susceptibility assessment are: elevation zone, slope gradient, slope curvature and geology; in the analysis at large scale also the weathered rock thickness, available only at this scale, assumes a relevant role. In both cases, the aggregation of multiple terrain computational units (TCU) into a larger terrain zoning unit (TZU) works best when focal statistic techniques are used with a characteristic dimension of the area of influence equal to 16 TCUs. (C) 2016 Elsevier B.V. All rights reserved.

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