Impact Factor (IF) - Thomson Reuters Web of KnowledgeSM)

2020: 1.500 - 5 years IF: 1.659

2019: 1.258 - 5 years IF: 1.610

2018: 1.152 - 5 years IF: 1.315

2017: 1.000 - 5 years IF: 1.000

2016: 0.938 - 5 years IF: 1.010

2015: 0.641 - 5 years IF: 0.673

2014: 0.628 - 5 years IF: 0.652

2013: 0.390 - 5 years IF: 0.504

2012: 0.605

2011: 0.468

2010: 0.309

2009: 0.136

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Volume 32 (2) - 2009


Landslide hazard assessment, vulnerability estimation and risk evaluation: an example from the Collazzone area (Central Umbria, Italy),

Pages 183-192


For the Collazzone area, central Umbria, landslide hazard was ascertained, landslide vulnerability was determined, and landslide risk was evaluated, for different scenarios. To ascertain landslide hazard, a probabilistic model was adopted that predicts where landslides will occur, how frequently they will occur, and how large they will be in a given area. For the purpose, a multi-temporal landslide inventory map prepared through the interpretation of five sets of aerial photographs and field surveys covering the period from 1941 to 2004 was exploited. Using a 10 m × 10 m DEM, the study area was partitioned into 894 slope units, and the probability of spatial landslide occurrence was obtained through discriminant analysis of thematic and environmental variables. For each slope unit, the probability of experiencing one or more landslides in different periods was determined adopting a Poisson probability model for the temporal occurrence of landslides. The probability of landslide size was obtained by analyzing the frequency-area statistics of landslides. Assuming independence, landslide hazard was ascertained as the joint probability of landslide size, of landslide temporal occurrence, and of landslide spatial occurrence. For the Umbria region, landslide vulnerability curves exist. The curves were established exploiting information on landslide damage to buildings and roads caused by individual landslides of the slide type. Assuming independence of hazard and vulnerability, and exploiting (i) the multi-temporal landslide inventory map, (ii) the obtained landslide hazard assessment, and (iii) the available landslide vulnerability curves, landslide risk to the road network was evaluated, for different scenarios. Results indicate that landslide risk can be determined quantitatively over large areas, provided adequate forecasting models are adopted and reliable landslide and thematic information is available.

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