Abstract
Forecasting through simulations the shape of lava invasions in a real topography represents a challenging problem, especially considering that the phenomenon usually evolves for a long time (e.g. from a few to hundreds of days) and on very large areas. In the latest years, Cellular Automata (CA) have been well recognized as a valid computational approach in lava flow modelling. In this paper we present some significant developments of SCIARA, a family of deterministic CA models of lava flows which are optimized for a specific scenario through the use of a parallel genetic algorithm. Following a calibration-validation approach, the model outcomes are compared with three real events of lava effusion. © Springer-Verlag Berlin Heidelberg 2007.
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Avolio, M. V., D’Ambrosio, D., Di Gregorio, S., Rongo, R., Spataro, W., & Trunfio, G. A. (2007). Modelling macroscopic phenomena with cellular automata and parallel genetic algorithms: An application to lava flows. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4487 LNCS, pp. 866–873). Springer Verlag. https://doi.org/10.1007/978-3-540-72584-8_114
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