Lava Invasion Susceptibility Hazard Mapping Through Cellular Automata

This work deals with a new methodology for the definition of volcanic susceptibly hazard maps through Cellular Automata and Genetic Algorithms. Specifically, the paper describes the proposed approach and presents the first results to the South­Eastern flank of Mt. Etna (Sicily, Italy). In particular, resulting hazard maps are characterized by a high degree of detail and allow for a punctual and accurate evaluation of the risk related to lava invasion. Cellular Automata (CA) are discrete dynamical systems, widely utilised for modelling and simulating complex systems, whose evolution can be described on the basis of local interactions. Well known examples are Lattice Gas Automata and Lattice Boltzmann models (cf. []), which are particularly suitable for modelling fluid dynamics at a microscopic level. However, many natural phenomena are difficult to be modelled at such scale, as they generally evolve on very large areas, thus needing a macroscopic level of description. Moreover, they may be also difficult to be modelled through standard approaches, such as differential equations (cf. []), and Macroscopic Cellular Automata [] can represent a valid alternative. Among the above mentioned phenomena, lava flows may involve serious dangers for people security and property, and their forecasting could significantly decrease this hazard, for instance by simulating lava paths and evaluating the effects of control works (e.g. embankments or channels). SCIARA [] is a family of deterministic Macroscopic CA models, specifically developed for simulating lava flows, in particular for the Etnean " aa " type, which are characterised by a relatively high viscosity degree. Numerous versions have been proposed, ranging from mainly empirical [] up to models which embed a proper physical description of the phenomenon and a more accurate control of its development []. However, experience showed that these latter allow for not considerable improvements in terms of