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Due to their relatively unpredictable characteristics,shallow landslides represent a risk for humaninfrastructures. Multiple shallow landslides can be triggered bywidespread intenseprecipitation events. The event of August 2005 in Switzerlandis used in order to propose a risk model to predict the expectednumber of landslides based on the precipitation amounts andlithological units. The spatial distribution of rainfall ischaracterized by merging data coming from operational weather radarsand a dense network of rain gauges with an artificial neuralnetwork. Lithologies are grouped into four main units, with similarcharacteristics. Then, from a landslide inventory containing more than5000 landslides, a probabilistic relation linking the precipitationamount and the lithology to the number of landslides ina 1 km<sup>2</sup> cell, is derived. In a next step, this relation isused to randomly redistribute the landslides using Monte Carlosimulations. The probability for a landslide to reach a building isassessed using stochastic geometry and the damage cost is assessedfrom the estimated mean damage cost using an exponential distributionto account for the variability. Although the model reproduces wellthe number of landslides, the number of affected buildings is underestimated. This seems to result from the humaninfluence on landslide occurrence. Such a model might be useful tocharacterize the risk resulting from shallow  landslides and itsvariability

Shallow landslide's stochastic risk modelling based on the precipitation event of August 2005 in Switzerland: results and implications