Predicting Burned Areas of Forest Fires: an Artificial Intelligence Approach

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Mauro Castelli and Leonardo Vanneschi and 
                 Ales Popovic",
  title =        "Predicting Burned Areas of Forest Fires: an Artificial
                 Intelligence Approach",
  journal =      "Fire Ecology",
  year =         "2015",
  volume =       "11",
  number =       "1",
  pages =        "106--118",
  keywords =     "genetic algorithms, genetic programming, geometric
                 semantic genetic programming",
  ISSN =         "1933-9747",
  publisher =    "The Association for Fire Ecology",
  DOI =          "doi:10.4996/fireecology.1101106",
  size =         "13 pages",
  abstract =     "Forest fires importantly influence our environment and
                 lives. The ability of accurately predicting the area
                 that may be involved in a forest fire event may help in
                 optimizing fire management efforts. Given the
                 complexity of the task, powerful computational tools
                 are needed for predicting the amount of area that will
                 be burned during a forest fire. The purpose of this
                 study was to develop an intelligent system based on
                 genetic programming for the prediction of burned areas,
                 using only data related to the forest under analysis
                 and meteorological data. We used geometric semantic
                 genetic programming based on recently defined geometric
                 semantic genetic operators for genetic programming.
                 Experimental results, achieved using a database of 517
                 forest fire events between 2000 and 2003, showed the
                 appropriateness of the proposed system for the
                 prediction of the burned areas. In particular, results
                 obtained with geometric semantic genetic programming
                 were significantly better than those produced by
                 standard genetic programming and other state of the art
                 machine learning methods on both training and
                 out-of-sample data. This study suggests that deeper
                 investigation of genetic programming in the field of
                 forest fires prediction may be productive.",
  notes =        "The Journal of the Association for Fire Ecology",

Genetic Programming entries for Mauro Castelli Leonardo Vanneschi Ales Popovic