Complex Systems Modeling with Cellular Automata and Genetic Algorithms: An Application to Lava Flows W. Spataro 1 , D. D’Ambrosio 1 , M.V. Avolio 1 , R. Rongo 2 , and S. Di Gregorio 1 1 Department of Mathematics, University of Calabria, Rende (CS), Italy 2 Department of Earth Sciences, University of Calabria, Rende (CS), Italy Abstract - Cellular Automata are parallel computational models which are capable to give rise to heterogeneous emergent behaviors notwithstanding simple local rules of evolution. In this review paper, a methodology for modeling complex natural systems through Macroscopic Cellular Automata is presented and applied to lava flow simulation. In particular, the 2001 Mt. Etna volcano Nicolosi (Italy) case study has been considered for model calibration, while the validation has been performed by considering further cases of study, which differ both in duration and emission rate. Parameter optimization was carried out by a Parallel Master-Slave Genetic Algorithm. Results have confirmed both the goodness of the simulation model and of the calibration algorithm. Eventually an application related to Civil Defense purposes is briefly described and proposed as a development. Keywords: Cellular Automata, Genetic Algorithms, Parallel Computing, Complex Systems, Modeling. 1 Introduction This review paper presents a methodology that is being successfully used for the efficient modeling and simulation of natural complex phenomena, such as lava flows, landslides, pyroclastic flows, etc. The main methodological framework relies on diverse computational paradigms such as Cellular Automata, Genetic Algorithms and Parallel Computing. Cellular Automata (CA) are discrete dynamical systems, widely utilized for modeling and simulating complex systems, whose evolution can be described in terms of local interactions. Regarding renowned examples of CA applications in fluid dynamics, Lattice Gas Automata and Lattice Boltzmann models [18], are particularly suitable for modeling phenomena at a microscopic scale. However, many natural phenomena are difficult to be modeled 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 modeled through standard approaches, such as differential equations [15]. In this case, Macroscopic Cellular Automata (MCA) [11] can represent a valid choice. Among the previous 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). A critical role is undoubtedly covered by the simulation model, which must be characterized by an elevated degree of reliability. In other words, the model must be properly calibrated, in order to reproduce a particular case of study at best, and validated on a sufficient number of different cases, in order to assess its goodness. SCIARA [3] is a family of deterministic MCA models, specifically developed for simulating lava flows, in particular for the Etnean “aa” type, which are characterized by a relatively high viscosity degree. Among the different releases, the last version, SCIARA-fv, derived from SCIARA-hex1 [7], was considered in this study, as in a first preliminary evaluation it demonstrated to be able to reproduce the qualitative behavior of Etnean lava flows. Moreover, it demands for lower computational requirements with respect to its predecessors, a mandatory model attribute that is particularly useful for the calibration phase, where an elevated number of simulations are generally needed. In the next Sections, Macroscopic Cellular Automata and Genetic Algorithms, heuristic search algorithms adopted for the model optimization, are briefly presented. The MCA model SCIARA-fv is briefly illustrated, together with calibration results on the 2001 Etnean Nicolosi (Italy) case study; subsequently, further cases of study are considered and results of model validation presented. Eventually, an ongoing application for Civil Defense purposes is discussed, and conclusions reported at the end. 2 Macroscopic Cellular Automata Macroscopic Cellular Automata were proposed by Di Gregorio and co-workers for the first time in 1982 to model the dynamics of macroscopic spatially extended systems, and firstly applied to the simulation of basaltic lava flows [8]. Afterwards, MCA were adopted for the simulation of many macroscopic phenomena, such as other kinds of lava flows [7], debris flows [14], as well as pyroclastic flows [1], traffic control [10], bioremediation processes [12] and, in their latest application, to combined subaerial-subaqueous landslides [2]. The MCA formalism introduces some extensions with respect to the classical definition of Cellular Automata. Major novelties regard the state of a cell, which is decomposed in “substates”, each one representing a particular feature (e.g. lava temperature, debris amount, etc)