A soft computing-based approach to spatio-temporal prediction

  • Authors:

  • Rú/bia E. O. Schultz;Tania M. Centeno;Gilles Selleron;Myriam R. Delgado

  • Affiliations:

  • CPGEI, Federal University of Technology -- Paraná/, 82230-901 Curitiba, PR, Brazil;CPGEI, Federal University of Technology -- Paraná/, 82230-901 Curitiba, PR, Brazil and DAINF, Federal University of Technology -- Paraná/, 82230-901 Curitiba, PR, Brazil;GEODE, University of Toulouse II (Le Mirail)/ 5, allé/es A. Machado, 31058 Toulouse Cedex 1, France;CPGEI, Federal University of Technology -- Paraná/, 82230-901 Curitiba, PR, Brazil and DAINF, Federal University of Technology -- Paraná/, 82230-901 Curitiba, PR, Brazil

  • Venue:
  • International Journal of Approximate Reasoning
  • Year:
  • 2009

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Abstract

This paper aims to incorporate intelligent mechanisms based on Soft Computing in Geographical Information Systems (GIS). The proposal here is to present a spatio-temporal prediction method of forestry evolution for a sequence of binary images by means of fuzzy inference systems (FIS), genetic algorithm (GA) and genetic programming (GP). The main inference is based on a fuzzy system which processes a set of crisp/fuzzy relations and infers a crisp relation representing the predicted image at a predefined date. The fuzzy system is formed by a fixed fuzzy rule base and a partition set that may be defined by an expert or optimized by means of a GA. Genetic programming may also be adopted to generate the size of predicted area used in the final stage of the inference process. The developed methodology is applied in regions of Venezuela, France and Guatemala to identify their forestry evolution trends. The proposed approaches are compared with other techniques to validate the system.