Title: A Comparison of Three Learning Methods to Predict N2O Fluxes and N Leaching
Authors: VILLA VIALANEIX NathalieFOLLADOR MarcoLEIP Adrian
Citation: Modèles et apprentissages en Sciences Humaines et Sociales 2010, ISBN : 978-952-60-2177-4 p. 57-66
Publisher: Multiprint Oy, Espoo, Finland
Publication Year: 2010
JRC N°: JRC59322
URI: http://halshs.archives-ouvertes.fr/docs/00/49/15/83/PDF/villavialaneix_etal_MASHS2010.pdf
http://publications.jrc.ec.europa.eu/repository/handle/JRC59322
Type: Articles in periodicals and books
Abstract: The environmental costs of intensive farming activities are often under-estimated or not included into the rural development plans, even though they play an important role in addressing future society¿s needs. This paper focus on the use of statistical learning methods to predict the N2O emissions and N leaching under several conservative scenarios, in order to provide an alternative approach to deterministic models at macro-scale. To that aim, three learning methods, namely neural networks (multilayer perceptrons), SVM and random forests, are compared and provide accurate solutions.
JRC Directorate:Sustainable Resources

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