[1] Forecasting the time, nature, and impact of future eruptions is difficult at volcanoes such as Mount Etna, in Italy, where eruptions occur from the summit and on the flanks, affecting areas distant from each other. Nonetheless, the identification and quantification of areas at risk from new eruptions are fundamental for mitigating potential human casualties and material damage. Here, we present new results from the application of a methodology to define flexible high-resolution lava invasion susceptibility maps based on a reliable computational model for simulating lava flows at Etna and on a validation procedure for assessing the correctness of susceptibility mapping in the study area. Furthermore, specific scenarios can be extracted at any time from the simulation database, for land use and civil defense planning in the long term, to quantify, in real time, the impact of an imminent eruption, and to assess the efficiency of protective measures.

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Cellular Automata (CA) are discrete and parallel computational models useful for simulating dynamic systems that evolve on the basis on local interactions. Some natural events, such as some types of landslides, fall into this type of phenomena and lend themselves well to be simulated with this approach. This paper describes the latest version of the SCIDDICA CA family models, specifically developed to simulate debris-flows type landslides. The latest model of the family, named SCIDDICA-SS3, inherits all the features of its predecessor, SCIDDICA-SS2, with the addition of a particular strategy to manage momentum. The introduction of the latter permits a better approximation of inertial effects that characterize some rapid debris flows. First simulations attempts of real landslides with SCIDDICA-SS3 have produced quite satisfactory results, comparable with the previous model

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