Abstract
We present some results of a computational study aimed at investigating the relationship between the spatio-temporal data used in the calibration phase and the consequent predictive ability of a Cellular Automata (CA) model. Our experiments concern a CA model for the simulation of urban dynamics which is typically used for predicting spatial scenarios of land-use. Since the model depends on a large number of parameters, we calibrate the CA using Cooperative Coevolutionary Particle Swarms, which is an effective approach for large-scale optimizations. Moreover, to cope with the relevant computational cost related to the high number of CA simulations required by our study, we exploits the computing power of Graphics Processing Units.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
White, R., Engelen, G.: High-resolution integrated modelling of the spatial dynamics of urban and regional systems. Computer, Environment and Urban Systems 24, 383–400 (2000)
Barredo, J.I., Kasanko, M., McCormick, N., Lavalle, C.: Modelling dynamic spatial processes: simulation of urban future scenarios through cellular automata. Landscape and Urban Planning 64, 145–160 (2003)
Blecic, I., Cecchini, A., Falk, M., Marras, S., Pyles, D.R., Spano, D., Trunfio, G.A.: Urban metabolism and climate change: A planning support system. Int. J. Applied Earth Observation and Geoinformation 26, 447–457 (2014)
Feng, Y., Liu, Y., Tong, X., Liu, M., Deng, S.: Modeling dynamic urban growth using cellular automata and particle swarm optimization rules. Landscape and Urban Planning 102, 188–196 (2011)
Rabbani, A., Aghababaee, H., Rajabi, M.A.: Modeling dynamic urban growth using hybrid cellular automata and particle swarm optimization. Journal of Applied Remote Sensing 6 (2012)
Blecic, I., Cecchini, A., Trunfio, G.A.: A comparison of evolutionary algorithms for automatic calibration of constrained cellular automata. In: Taniar, D., Gervasi, O., Murgante, B., Pardede, E., Apduhan, B.O. (eds.) ICCSA 2010, Part I. LNCS, vol. 6016, pp. 166–181. Springer, Heidelberg (2010)
Blecic, I., Cecchini, A., Trunfio, G.A.: Cellular automata simulation of urban dynamics through GPGPU. The Journal of Supercomputing 65, 614–629 (2013)
van den Bergh, F., Engelbrecht, A.P.: A cooperative approach to particle swarm optimization. IEEE Trans. Evolutionary Computation 8, 225–239 (2004)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)
Wu, F.: SimLand: A prototype to simulate land conversion through the integrated GIS and ca with ahp-derived transition rules. International Journal of Geographical Information Science 12, 63–82 (1998)
Blecic, I., Cecchini, A., Trunfio, G.A.: Fast and accurate optimization of a GPU-accelerated CA urban model through cooperative coevolutionary particle swarms. Procedia Computer Science 29, 1631–1643 (2014)
van Vliet, J., Bregt, A.K., Hagen-Zanker, A.: Revisiting Kappa to account for change in the accuracy assessment of land-use change models. Ecological Modelling 222, 1367–1375 (2011)
Blecic, I., Cecchini, A., Trunfio, G.A.: A decision support tool coupling a causal model and a multi-objective genetic algorithm. Appl. Intell. 26, 125–137 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Blecic, I., Cecchini, A., Trunfio, G.A. (2014). Training Cellular Automata to Simulate Urban Dynamics: A Computational Study Based on GPGPU and Swarm Intelligence. In: Wąs, J., Sirakoulis, G.C., Bandini, S. (eds) Cellular Automata. ACRI 2014. Lecture Notes in Computer Science, vol 8751. Springer, Cham. https://doi.org/10.1007/978-3-319-11520-7_31
Download citation
DOI: https://doi.org/10.1007/978-3-319-11520-7_31
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11519-1
Online ISBN: 978-3-319-11520-7
eBook Packages: Computer ScienceComputer Science (R0)