@article{garzonPredictingHabitatSuitability2006,
  title = {Predicting Habitat Suitability with Machine Learning Models: {{The}} Potential Area of {{Pinus}} Sylvestris {{L}}. in the {{Iberian Peninsula}}},
  author = {Garz{\'o}n, Marta B. and Blazek, Radim and Neteler, Markus and Dios, Rut S. and Ollero, Helios S. and Furlanello, Cesare},
  year = {2006},
  month = aug,
  volume = {197},
  pages = {383--393},
  issn = {0304-3800},
  doi = {10.1016/j.ecolmodel.2006.03.015},
  abstract = {We present a modelling framework for predicting forest areas. The framework is obtained by integrating a machine learning software suite within the GRASS Geographical Information System (GIS) and by providing additional methods for predictive habitat modelling. Three machine learning techniques (Tree-Based Classification, Neural Networks and Random Forest) are available in parallel for modelling from climatic and topographic variables. Model evaluation and parameter selection are measured by sensitivity-specificity ROC analysis, while the final presence and absence maps are obtained through maximisation of the kappa statistic. The modelling framework is applied at a resolution of 1 km with Iberian subpopulations of Pinus sylvestris L. forests. For this data set, the most accurate algorithm is Breiman's random forest, an ensemble method which provides automatic combination of tree-classifiers trained on bootstrapped subsamples and randomised variable sets. All models show a potential area of P. sylvestris for the Iberian Peninsula which is larger than the present one, a result corroborated by regional pollen analyses.},
  journal = {Ecological Modelling},
  keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-608546,~to-add-doi-URL,auc,cart,habitat-suitability,iberian-peninsula,kappa,machine-learning,neural-networks,pinus-sylvestris,random-forest},
  lccn = {INRMM-MiD:c-608546},
  number = {3-4}
}

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S.","Furlanello, C."],"year":2006,"bibtype":"article","biburl":"https://sharefast.me/php/download.php?id=zOUKvA&token=29","bibdata":{"bibtype":"article","type":"article","title":"Predicting Habitat Suitability with Machine Learning Models: The Potential Area of Pinus Sylvestris L. in the Iberian Peninsula","author":[{"propositions":[],"lastnames":["Garzón"],"firstnames":["Marta","B."],"suffixes":[]},{"propositions":[],"lastnames":["Blazek"],"firstnames":["Radim"],"suffixes":[]},{"propositions":[],"lastnames":["Neteler"],"firstnames":["Markus"],"suffixes":[]},{"propositions":[],"lastnames":["Dios"],"firstnames":["Rut","S."],"suffixes":[]},{"propositions":[],"lastnames":["Ollero"],"firstnames":["Helios","S."],"suffixes":[]},{"propositions":[],"lastnames":["Furlanello"],"firstnames":["Cesare"],"suffixes":[]}],"year":"2006","month":"August","volume":"197","pages":"383–393","issn":"0304-3800","doi":"10.1016/j.ecolmodel.2006.03.015","abstract":"We present a modelling framework for predicting forest areas. 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The framework is obtained by integrating a machine learning software suite within the GRASS Geographical Information System (GIS) and by providing additional methods for predictive habitat modelling. Three machine learning techniques (Tree-Based Classification, Neural Networks and Random Forest) are available in parallel for modelling from climatic and topographic variables. Model evaluation and parameter selection are measured by sensitivity-specificity ROC analysis, while the final presence and absence maps are obtained through maximisation of the kappa statistic. The modelling framework is applied at a resolution of 1 km with Iberian subpopulations of Pinus sylvestris L. forests. For this data set, the most accurate algorithm is Breiman's random forest, an ensemble method which provides automatic combination of tree-classifiers trained on bootstrapped subsamples and randomised variable sets. 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