A multi-agent model system for land-use change simulation

  • Authors:

  • CéLia G. Ralha;Carolina G. Abreu;CáSsio G. C. Coelho;Alexandre Zaghetto;Bruno Macchiavello;Ricardo B. Machado

  • Affiliations:

  • Computer Science Department, Institute of Exact Sciences, University of Brasília, P.O. Box 4466, Zip Code 70.904-970 Brasília, DF, Brazil;Computer Science Department, Institute of Exact Sciences, University of Brasília, P.O. Box 4466, Zip Code 70.904-970 Brasília, DF, Brazil and The Brazilian Institute of Environment and R ...;Computer Science Department, Institute of Exact Sciences, University of Brasília, P.O. Box 4466, Zip Code 70.904-970 Brasília, DF, Brazil;Computer Science Department, Institute of Exact Sciences, University of Brasília, P.O. Box 4466, Zip Code 70.904-970 Brasília, DF, Brazil;Computer Science Department, Institute of Exact Sciences, University of Brasília, P.O. Box 4466, Zip Code 70.904-970 Brasília, DF, Brazil;Zoology Department, Institute of Biological Sciences, University of Brasília, Campus Darcy Ribeiro, Zip Code 70.910-900 Brasília, Brazil

  • Venue:
  • Environmental Modelling & Software
  • Year:
  • 2013

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Abstract

This paper presents a multi-agent model system to characterize land-use change dynamics. The replicable parameterization process should be useful to the development of simulation frameworks, important to environmental policy makers to analyze different scenarios during decision making process. The methodological two-fold approach intends to form a solid backbone based on: (i) the systematic and structured empirical characterization of the model; and (ii) the conceptual structure definition according to the agent-based model documentation protocol - Overview, Design concepts and Details. A multi-agent system for land-use change simulation was developed to validate the model, which is illustrated with a case study of the Brazilian Cerrado using LANDSAT ETM images. The simulation results prove the model importance with a figure of merit greater than 50%, what means the amount of correctly predicted change is larger than the sum of any type of error. The results are very good compared with nine popular peer-reviewed land change models.