Evolutionary Applications to Cellular Automata Models for Volcano Risk Mitigation

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 445)


A GPGPU accelerated evolutionary computation-based decision support system for defining and optimizing volcanic hazard mitigation interventions is proposed. Specifically, the new Cellular Automata numerical model SCIARA-fv3 for simulating lava flows at Mt Etna (Italy) and Parallel Genetic Algorithms (PGA) have been applied for optimizing protective measures construction by morphological evolution. A case study is considered, where PGA are applied for the optimization of the position, orientation and extension of earth barriers built to protect a touristic facility located near the summit of Mt. Etna (Italy) volcano which was interested by the 2001 lava eruption. The methodology has produced extremely positive results and, in our opinion, can be applied within a broader risk assessment framework, having immediate and far reaching implications both in land use and civil defense planning.


Evolutionary computation Parallel genetic algorithms Decision support system Cellular automata Morphological evolution 



This work was partially funded by the European Commission \(-\) European Social Fund and by the Regione Calabria (Italy). Authors gratefully acknowledge the support of NVIDIA Corporation for this research.


  1. 1.
    Di Gregorio, S., Serra, R.: An empirical method for modelling and simulating some complex macroscopic phenomena by cellular automata. Future Gener. Comput. Syst. 16(2–3), 259–271 (1999)CrossRefGoogle Scholar
  2. 2.
    Von Neumann, J.: Theory Self-reproducing Automata. University of Illinois Press, Champaign (1966)Google Scholar
  3. 3.
    Barberi, F., Brondi, F., Carapezza, M., Cavarra, L., Murgia, C.: Earthen barriers to control lava flows in the 2001 eruption of Mt. Etna. J. Volcanol. Geoth. Res. 123, 231–243 (2003)CrossRefGoogle Scholar
  4. 4.
    Colombrita, R.: Methodology for the construction of earth barriers to divert lava flows: the Mt. Etna 1983 eruption. Bull. Volcanol. 47(4), 1009–1038 (1984)CrossRefGoogle Scholar
  5. 5.
    Bentley, P.: An introduction to evolutionary design by computers (chap. 1). In: Bentley, P.J. (ed.) Evolutionary Design by Computers, pp. 1–73. Morgan Kaufman, San Francisco (1999)Google Scholar
  6. 6.
    Sims, K.: Evolving 3D morphology and behavior by competition. In: Proceedings of Artificial Life IV, pp. 28–39. MIT Press (1994)Google Scholar
  7. 7.
    Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. The MIT Press, Cambridge (1992)Google Scholar
  8. 8.
    D’Ambrosio, D., Spataro, W.: Parallel evolutionary modelling of geological processes. J. Parallel Comput. 33(3), 186–212 (2007)CrossRefMathSciNetGoogle Scholar
  9. 9.
    D’Ambrosio, D., Rongo, R., Spataro, W., Trunfio, G.A.: Optimizing cellular automata through a meta-model assisted memetic algorithm. In: Coello, C.A.C., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds.) PPSN 2012, Part II. LNCS, vol. 7492, pp. 317–326. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  10. 10.
    D’Ambrosio, D., Spataro, W., Parise, R., Rongo, R., Filippone, G., Spataro, D., Iovine, G., Marocco, D.: Lava flow modeling by the sciara-fv3 parallel numerical code. In: 22nd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, pp. 330–338 (2014)Google Scholar
  11. 11.
    Radu, V.: Application. In: Radu, V. (ed.) Stochastic Modeling of Thermal Fatigue Crack Growth. ACM, vol. 1, pp. 63–70. Springer, Heidelberg (2015)Google Scholar
  12. 12.
    Hinton, G.E., Nowlan, S.J.: How learning can guide evolution. Complex Syst. 1, 495–502 (1987)zbMATHGoogle Scholar
  13. 13.
    Peters, J.F.: Topology of digital images: basic ingredients. In: Peters, J.F. (ed.) Topology of Digital Images. ISRL, vol. 63, pp. 1–76. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  14. 14.
    D’Ambrosio, D., Rongo, R., Spataro, W., Trunfio, G.A.: Meta-model assisted evolutionary optimization of cellular automata: an application to the SCIARA model. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Waśniewski, J. (eds.) PPAM 2011, Part II. LNCS, vol. 7204, pp. 533–542. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  15. 15.
    Barberi, F., Carapezza, M.L.: The control of lava flows at Mt. Etna. In: Bonaccorso, A., Calvari, S., Coltelli, M., Del Negro, C., Falsaperla, S. (eds.) Mt. Etna: Volcano Laboratory, 357th edn, p. 369. American Geophysical Union, Washington, D.C. (2004)Google Scholar
  16. 16.
    Blecic, I., Cecchini, A., Trunfio, G.: Cellular automata simulation of urban dynamics through GPGPU. J. Supercomput. 65, 614–629 (2013)CrossRefGoogle Scholar
  17. 17.
    D’Ambrosio, D., Filippone, G., Marocco, D., Rongo, R., Spataro, W.: Efficient application of GPGPU for lava flow hazard mapping. J. Supercomput. 65(2), 630–644 (2013)CrossRefGoogle Scholar
  18. 18.
    Di Gregorio, S., Filippone, G., Spataro, W., Trunfio, G.A.: Accelerating wildfire susceptibility mapping through GPGPU. J. Parallel Distrib. Comput. 73(8), 1183–1194 (2013)CrossRefGoogle Scholar
  19. 19.
    Fujita, E., Hidaka, M., Goto, A., Umino, S.: Simulations of measures to control lava flows. Bull. Volcanol. 71, 401–408 (2009)CrossRefGoogle Scholar
  20. 20.
    Parise, R., D’Ambrosio, D., Spingola, G., Filippone, G., Rongo, R., Trunfio, G.A., Spataro, W.: Swii2, a HTML5/WebGL application for cellular automata debris flows simulation. In: Sirakoulis, G.C., Bandini, S. (eds.) ACRI 2012. LNCS, vol. 7495, pp. 444–453. Springer, Heidelberg (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Mathematics and Computer ScienceUniversity of CalabriaRendeItaly
  2. 2.CNR-IRPI, Sezione di CosenzaRendeItaly

Personalised recommendations