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Potential Distribution Modelling Using Machine Learning

  • Ana C. Lorena
  • Marinez F. de Siqueira
  • Renato De Giovanni
  • André C. P. L. F. de Carvalho
  • Ronaldo C. Prati
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5027)

Abstract

Potential distribution modelling has been widely used to predict and to understand the geographical distribution of species. These models are generally produced by retrieving the environmental conditions where the species is known to be present or absent and feeding this data into a modelling algorithm. This paper investigates the use of Machine Learning techniques in the potential distribution modelling of plant species Stryphnodendron obovatum Benth (MIMOSACEAE). Three techniques were used: Support Vector Machines, Genetic Algorithms and Decision Trees. Each technique was able to extract a different representation of the relations between the environmental conditions and the distribution profile of the species being considered.

Keywords

Ecological niche modelling Potential distribution modelling Machine Learning 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Ana C. Lorena
    • 1
  • Marinez F. de Siqueira
    • 2
  • Renato De Giovanni
    • 2
  • André C. P. L. F. de Carvalho
    • 3
  • Ronaldo C. Prati
    • 3
  1. 1.Centro de Matemática, Computação e CogniçãoUniversidade Federal do ABCSanto AndréBrazil
  2. 2.Centro de Referência em Informação AmbientalCampinasBrazil
  3. 3.Instituto de Ciências Matemáticas e de ComputaçãoUniversidade de São PauloSão CarlosBrazil

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