T. Bornaetxea(1), M. Rossi(2), I. Marchesini(2), M. Alvioli(2), 2018, Effective surveyed area and its role in statistical landslide susceptibility assessments, Natural hazards and earth system sciences (Online) 18 (2018): 2455–2469. doi_10.5194/nhess-18-2455-2018,
URL: http://www.cnr.it/prodotto/i/386355

Geomorphological field mapping is a conventional method to prepare landslide inventories. The approach is typically hampered by the accessibility and visibility, during field campaigns for landslide mapping, of the different portions of the study area. Statistical significance of landlside susceptibility maps can be significantly reduced if the classification algorithm is trained in unsurveyed regions of the study area, for which landslide absence is tipically assumed, while ingnorance about landslide presence should actually be aknowledged. We compare different landslide susceptibility zonations obtained by training the classification model either in the entire study area or in the only portion of the area that was actually surveyed, which we name effective surveyed area. The latter was delineated by an automatic procedure specifically devised for the purpose, which uses information gathered during surveys, along with landslide locations. The method was tested in Gipuzkoa Province (Basque Country), North of the Iberian Peninsula, where digital thematic maps were available and a landslide survey was performed. We prepared the landslide susceptibility maps and the associated uncertainty within a logistic regression model, using both slope units and regular grid cells as reference mapping unit. Results indicate that the use of effective surveyed area for landslide susceptibility zonation is a valid approach to minimize the limitations stemming from unsurveyed regions at landslide mapping time. Use of slope units as mapping units, instead of grid cells, mitigates the uncertainties introduced by training the automatic classifier within the entire study area. Our method pertains to data preparation and, as such, the relevance of our conclusions is not limited to the logistic regression but are valid for virtually all the existing multivariate landslide susceptibility models.

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